Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.

Intellectual factors in false memories of patients with schizophrenia.

PubMed

Zhu, Bi; Chen, Chuansheng; Loftus, Elizabeth F; Dong, Qi; Lin, Chongde; Li, Jun

2018-07-01

The current study explored the intellectual factors in false memories of 139 patients with schizophrenia, using a recognition task and an IQ test. The full-scale IQ score of the participants ranged from 57 to 144 (M = 100, SD = 14). The full IQ score had a negative correlation with false recognition in patients with schizophrenia, and positive correlations with high-confidence true recognition and discrimination rates. Further analyses with the subtests' scores revealed that false recognition was negatively correlated with scores of performance IQ (and one of its subtests: picture arrangement), whereas true recognition was positively correlated with scores of verbal IQ (and two of its subtests: information and digit span). High-IQ patients had less false recognition (overall or high-confidence false recognition), more high-confidence true recognition, and higher discrimination abilities than those with low IQ. These findings contribute to a better understanding of the cognitive mechanism in false memory of patients with schizophrenia, and are of practical relevance to the evaluation of memory reliability in patients with different intellectual levels. Copyright © 2018 Elsevier B.V. All rights reserved.

Face Recognition by Metropolitan Police Super-Recognisers.

PubMed

Robertson, David J; Noyes, Eilidh; Dowsett, Andrew J; Jenkins, Rob; Burton, A Mike

2016-01-01

Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability-a group that has come to be known as 'super-recognisers'. The Metropolitan Police Force (London) recruits 'super-recognisers' from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police 'super-recognisers' perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition.

The Role of the Association in Recognition Memory.

ERIC Educational Resources Information Center

Underwood, Benton J.

The purpose of the eight experiments was to assess the role which associations between two words played in recognition decisions. The evidence on weak associations established in the laboratory indicated that association was playing a small role, but that the recognition performance on pairs of words was highly predictable from frequency…

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Effects of Age and Working Memory Capacity on Speech Recognition Performance in Noise Among Listeners With Normal Hearing.

PubMed

Gordon-Salant, Sandra; Cole, Stacey Samuels

2016-01-01

This study aimed to determine if younger and older listeners with normal hearing who differ on working memory span perform differently on speech recognition tests in noise. Older adults typically exhibit poorer speech recognition scores in noise than younger adults, which is attributed primarily to poorer hearing sensitivity and more limited working memory capacity in older than younger adults. Previous studies typically tested older listeners with poorer hearing sensitivity and shorter working memory spans than younger listeners, making it difficult to discern the importance of working memory capacity on speech recognition. This investigation controlled for hearing sensitivity and compared speech recognition performance in noise by younger and older listeners who were subdivided into high and low working memory groups. Performance patterns were compared for different speech materials to assess whether or not the effect of working memory capacity varies with the demands of the specific speech test. The authors hypothesized that (1) normal-hearing listeners with low working memory span would exhibit poorer speech recognition performance in noise than those with high working memory span; (2) older listeners with normal hearing would show poorer speech recognition scores than younger listeners with normal hearing, when the two age groups were matched for working memory span; and (3) an interaction between age and working memory would be observed for speech materials that provide contextual cues. Twenty-eight older (61 to 75 years) and 25 younger (18 to 25 years) normal-hearing listeners were assigned to groups based on age and working memory status. Northwestern University Auditory Test No. 6 words and Institute of Electrical and Electronics Engineers sentences were presented in noise using an adaptive procedure to measure the signal-to-noise ratio corresponding to 50% correct performance. Cognitive ability was evaluated with two tests of working memory (Listening Span Test and Reading Span Test) and two tests of processing speed (Paced Auditory Serial Addition Test and The Letter Digit Substitution Test). Significant effects of age and working memory capacity were observed on the speech recognition measures in noise, but these effects were mediated somewhat by the speech signal. Specifically, main effects of age and working memory were revealed for both words and sentences, but the interaction between the two was significant for sentences only. For these materials, effects of age were observed for listeners in the low working memory groups only. Although all cognitive measures were significantly correlated with speech recognition in noise, working memory span was the most important variable accounting for speech recognition performance. The results indicate that older adults with high working memory capacity are able to capitalize on contextual cues and perform as well as young listeners with high working memory capacity for sentence recognition. The data also suggest that listeners with normal hearing and low working memory capacity are less able to adapt to distortion of speech signals caused by background noise, which requires the allocation of more processing resources to earlier processing stages. These results indicate that both younger and older adults with low working memory capacity and normal hearing are at a disadvantage for recognizing speech in noise.

Multiple template-based image matching using alpha-rooted quaternion phase correlation

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2010-04-01

In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels. Noise and clutter cause the formation of false features which can degrade recognition performance. To address this problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation. ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued images. We generate recognition performance results on publicly-available logo imagery, and compare recognition results to results generated from standard approaches. We show that small deviations in reference templates of sameclass logos can lead to improved recognition performance using the joint matching inherent in ARQPC.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Individual differences in forced-choice recognition memory: partitioning contributions of recollection and familiarity.

PubMed

Migo, Ellen M; Quamme, Joel R; Holmes, Selina; Bendell, Andrew; Norman, Kenneth A; Mayes, Andrew R; Montaldi, Daniela

2014-01-01

In forced-choice recognition memory, two different testing formats are possible under conditions of high target-foil similarity: Each target can be presented alongside foils similar to itself (forced-choice corresponding; FCC), or alongside foils similar to other targets (forced-choice noncorresponding; FCNC). Recent behavioural and neuropsychological studies suggest that FCC performance can be supported by familiarity whereas FCNC performance is supported primarily by recollection. In this paper, we corroborate this finding from an individual differences perspective. A group of older adults were given a test of FCC and FCNC recognition for object pictures, as well as standardized tests of recall, recognition, and IQ. Recall measures were found to predict FCNC, but not FCC performance, consistent with a critical role for recollection in FCNC only. After the common influence of recall was removed, standardized tests of recognition predicted FCC, but not FCNC performance. This is consistent with a contribution of only familiarity in FCC. Simulations show that a two-process model, where familiarity and recollection make separate contributions to recognition, is 10 times more likely to give these results than a single-process model. This evidence highlights the importance of recognition memory test design when examining the involvement of recollection and familiarity.

Recognizing Dynamic Faces in Malaysian Chinese Participants.

PubMed

Tan, Chrystalle B Y; Sheppard, Elizabeth; Stephen, Ian D

2016-03-01

High performance level in face recognition studies does not seem to be replicable in real-life situations possibly because of the artificial nature of laboratory studies. Recognizing faces in natural social situations may be a more challenging task, as it involves constant examination of dynamic facial motions that may alter facial structure vital to the recognition of unfamiliar faces. Because of the incongruences of recognition performance, the current study developed stimuli that closely represent natural social situations to yield results that more accurately reflect observers' performance in real-life settings. Naturalistic stimuli of African, East Asian, and Western Caucasian actors introducing themselves were presented to investigate Malaysian Chinese participants' recognition sensitivity and looking strategies when performing a face recognition task. When perceiving dynamic facial stimuli, participants fixated most on the nose, followed by the mouth then the eyes. Focusing on the nose may have enabled participants to gain a more holistic view of actors' facial and head movements, which proved to be beneficial in recognizing identities. Participants recognized all three races of faces equally well. The current results, which differed from a previous static face recognition study, may be a more accurate reflection of observers' recognition abilities and looking strategies. © The Author(s) 2015.

Interplay between affect and arousal in recognition memory.

PubMed

Greene, Ciara M; Bahri, Pooja; Soto, David

2010-07-23

Emotional states linked to arousal and mood are known to affect the efficiency of cognitive performance. However, the extent to which memory processes may be affected by arousal, mood or their interaction is poorly understood. Following a study phase of abstract shapes, we altered the emotional state of participants by means of exposure to music that varied in both mood and arousal dimensions, leading to four different emotional states: (i) positive mood-high arousal; (ii) positive mood-low arousal; (iii) negative mood-high arousal; (iv) negative mood-low arousal. Following the emotional induction, participants performed a memory recognition test. Critically, there was an interaction between mood and arousal on recognition performance. Memory was enhanced in the positive mood-high arousal and in the negative mood-low arousal states, relative to the other emotional conditions. Neither mood nor arousal alone but their interaction appears most critical to understanding the emotional enhancement of memory.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Electrophysiological distinctions between recognition memory with and without awareness

PubMed Central

Ko, Philip C.; Duda, Bryant; Hussey, Erin P.; Ally, Brandon A.

2013-01-01

The influence of implicit memory representations on explicit recognition may help to explain cases of accurate recognition decisions made with high uncertainty. During a recognition task, implicit memory may enhance the fluency of a test item, biasing decision processes to endorse it as “old”. This model may help explain recognition-without-identification, a remarkable phenomenon in which participants make highly accurate recognition decisions despite the inability to identify the test item. The current study investigated whether recognition-without-identification for pictures elicits a similar pattern of neural activity as other types of accurate recognition decisions made with uncertainty. Further, this study also examined whether recognition-without-identification for pictures could be attained by the use of perceptual and conceptual information from memory. To accomplish this, participants studied pictures and then performed a recognition task under difficult viewing conditions while event-related potentials (ERPs) were recorded. Behavioral results showed that recognition was highly accurate even when test items could not be identified, demonstrating recognition-without identification. The behavioral performance also indicated that recognition-without-identification was mediated by both perceptual and conceptual information, independently of one another. The ERP results showed dramatically different memory related activity during the early 300 to 500 ms epoch for identified items that were studied compared to unidentified items that were studied. Similar to previous work highlighting accurate recognition without retrieval awareness, test items that were not identified, but correctly endorsed as “old,” elicited a negative posterior old/new effect (i.e., N300). In contrast, test items that were identified and correctly endorsed as “old,” elicited the classic positive frontal old/new effect (i.e., FN400). Importantly, both of these effects were elicited under conditions when participants used perceptual information to make recognition decisions. Conceptual information elicited very different ERPs than perceptual information, showing that the informational wealth of pictures can evoke multiple routes to recognition even without awareness of memory retrieval. These results are discussed within the context of current theories regarding the N300 and the FN400. PMID:23287567

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Face Recognition by Metropolitan Police Super-Recognisers

PubMed Central

Robertson, David J.; Noyes, Eilidh; Dowsett, Andrew J.; Jenkins, Rob; Burton, A. Mike

2016-01-01

Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability—a group that has come to be known as ‘super-recognisers’. The Metropolitan Police Force (London) recruits ‘super-recognisers’ from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police ‘super-recognisers’ perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition. PMID:26918457

Recognition of face and non-face stimuli in autistic spectrum disorder.

PubMed

Arkush, Leo; Smith-Collins, Adam P R; Fiorentini, Chiara; Skuse, David H

2013-12-01

The ability to remember faces is critical for the development of social competence. From childhood to adulthood, we acquire a high level of expertise in the recognition of facial images, and neural processes become dedicated to sustaining competence. Many people with autism spectrum disorder (ASD) have poor face recognition memory; changes in hairstyle or other non-facial features in an otherwise familiar person affect their recollection skills. The observation implies that they may not use the configuration of the inner face to achieve memory competence, but bolster performance in other ways. We aimed to test this hypothesis by comparing the performance of a group of high-functioning unmedicated adolescents with ASD and a matched control group on a "surprise" face recognition memory task. We compared their memory for unfamiliar faces with their memory for images of houses. To evaluate the role that is played by peripheral cues in assisting recognition memory, we cropped both sets of pictures, retaining only the most salient central features. ASD adolescents had poorer recognition memory for faces than typical controls, but their recognition memory for houses was unimpaired. Cropping images of faces did not disproportionately influence their recall accuracy, relative to controls. House recognition skills (cropped and uncropped) were similar in both groups. In the ASD group only, performance on both sets of task was closely correlated, implying that memory for faces and other complex pictorial stimuli is achieved by domain-general (non-dedicated) cognitive mechanisms. Adolescents with ASD apparently do not use domain-specialized processing of inner facial cues to support face recognition memory. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

Orthographic recognition in late adolescents: an assessment through event-related brain potentials.

PubMed

González-Garrido, Andrés Antonio; Gómez-Velázquez, Fabiola Reveca; Rodríguez-Santillán, Elizabeth

2014-04-01

Reading speed and efficiency are achieved through the automatic recognition of written words. Difficulties in learning and recognizing the orthography of words can arise despite reiterative exposure to texts. This study aimed to investigate, in native Spanish-speaking late adolescents, how different levels of orthographic knowledge might result in behavioral and event-related brain potential differences during the recognition of orthographic errors. Forty-five healthy high school students were selected and divided into 3 equal groups (High, Medium, Low) according to their performance on a 5-test battery of orthographic knowledge. All participants performed an orthographic recognition task consisting of the sequential presentation of a picture (object, fruit, or animal) followed by a correctly, or incorrectly, written word (orthographic mismatch) that named the picture just shown. Electroencephalogram (EEG) recording took place simultaneously. Behavioral results showed that the Low group had a significantly lower number of correct responses and increased reaction times while processing orthographical errors. Tests showed significant positive correlations between higher performance on the experimental task and faster and more accurate reading. The P150 and P450 components showed higher voltages in the High group when processing orthographic errors, whereas N170 seemed less lateralized to the left hemisphere in the lower orthographic performers. Also, trials with orthographic errors elicited a frontal P450 component that was only evident in the High group. The present results show that higher levels of orthographic knowledge correlate with high reading performance, likely because of faster and more accurate perceptual processing, better visual orthographic representations, and top-down supervision, as the event-related brain potential findings seem to suggest.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

PubMed

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

NASA Astrophysics Data System (ADS)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Exploring the feasibility of iris recognition for visible spectrum iris images obtained using smartphone camera

NASA Astrophysics Data System (ADS)

Trokielewicz, Mateusz; Bartuzi, Ewelina; Michowska, Katarzyna; Andrzejewska, Antonina; Selegrat, Monika

2015-09-01

In the age of modern, hyperconnected society that increasingly relies on mobile devices and solutions, implementing a reliable and accurate biometric system employing iris recognition presents new challenges. Typical biometric systems employing iris analysis require expensive and complicated hardware. We therefore explore an alternative way using visible spectrum iris imaging. This paper aims at answering several questions related to applying iris biometrics for images obtained in the visible spectrum using smartphone camera. Can irides be successfully and effortlessly imaged using a smartphone's built-in camera? Can existing iris recognition methods perform well when presented with such images? The main advantage of using near-infrared (NIR) illumination in dedicated iris recognition cameras is good performance almost independent of the iris color and pigmentation. Are the images obtained from smartphone's camera of sufficient quality even for the dark irides? We present experiments incorporating simple image preprocessing to find the best visibility of iris texture, followed by a performance study to assess whether iris recognition methods originally aimed at NIR iris images perform well with visible light images. To our best knowledge this is the first comprehensive analysis of iris recognition performance using a database of high-quality images collected in visible light using the smartphones flashlight together with the application of commercial off-the-shelf (COTS) iris recognition methods.

Individual differences in forced-choice recognition memory: Partitioning contributions of recollection and familiarity

PubMed Central

Migo, Ellen M.; Quamme, Joel R.; Holmes, Selina; Bendell, Andrew; Norman, Kenneth A.; Mayes, Andrew R.; Montaldi, Daniela

2014-01-01

In forced-choice recognition memory, two different testing formats are possible under conditions of high target-foil similarity: each target can be presented alongside foils similar to itself (forced-choice corresponding; FCC), or alongside foils similar to other targets (forced-choice non-corresponding; FCNC).Recent behavioural and neuropsychological studies suggest that FCC performance can be supported by familiarity whereas FCNC performance is supported primarily by recollection. In this paper, we corroborate this finding from an individual differences perspective. A group of older adults were given a test of FCC and FCNC recognition for object pictures, as well as standardised tests of recall, recognition and IQ. Recall measures were found to predict FCNC, but not FCC performance, consistent with a critical role for recollection in FCNC only. After the common influence of recall was removed, standardised tests of recognition predicted FCC, but not FCNC performance. This is consistent with a contribution of only familiarity in FCC. Simulations show that a two process model, where familiarity and recollection make separate contributions to recognition, is ten times more likely to give these results than a single-process model. This evidence highlights the importance of recognition memory test design when examining the involvement of recollection and familiarity. PMID:24796268

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

DTIC Science & Technology

2013-01-01

M. Ahmadi, and M. Shridhar, “ Handwritten Numeral Recognition with Multiple Features and Multistage Classifiers,” Proc. IEEE Int’l Symp. Circuits...ARTICLE (Post Print) 3. DATES COVERED (From - To) SEP 2011 – SEP 2013 4. TITLE AND SUBTITLE A PARALLEL NEUROMORPHIC TEXT RECOGNITION SYSTEM AND ITS...research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition

Random-Profiles-Based 3D Face Recognition System

PubMed Central

Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

2014-01-01

In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

The role of perceptual load in object recognition.

PubMed

Lavie, Nilli; Lin, Zhicheng; Zokaei, Nahid; Thoma, Volker

2009-10-01

Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were unaffected by a change in the distracter object view under conditions of low perceptual load. These results were found both with repetition priming measures of distracter recognition and with performance on a surprise recognition memory test. The results support load theory proposals that distracter recognition critically depends on the level of perceptual load. The implications for the role of attention in object recognition theories are discussed. PsycINFO Database Record (c) 2009 APA, all rights reserved.

Translation Recognition in Highly Proficient Hindi-English Bilinguals: The Influence of Different Scripts but Connectable Phonologies

ERIC Educational Resources Information Center

Sunderman, Gretchen L.; Priya, Kanu

2012-01-01

This study investigates the phonological nature of the lexical links in the bilingual lexicon using different-script bilinguals. Highly proficient Hindi-English bilinguals performed a translation recognition task (i.e., decide whether two words presented sequentially are a correct translation pair). For the critical trials, the second word was a…

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

Fusion of smartphone motion sensors for physical activity recognition.

PubMed

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

2014-06-10

For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

Consonant-recognition patterns and self-assessment of hearing handicap.

PubMed

Hustedde, C G; Wiley, T L

1991-12-01

Two companion experiments were conducted with normal-hearing subjects and subjects with high-frequency, sensorineural hearing loss. In Experiment 1, the validity of a self-assessment device of hearing handicap was evaluated in two groups of hearing-impaired listeners with significantly different consonant-recognition ability. Data for the Hearing Performance Inventory--Revised (Lamb, Owens, & Schubert, 1983) did not reveal differences in self-perceived handicap for the two groups of hearing-impaired listeners; it was sensitive to perceived differences in hearing abilities for listeners who did and did not have a hearing loss. Experiment 2 was aimed at evaluation of consonant error patterns that accounted for observed group differences in consonant-recognition ability. Error patterns on the Nonsense-Syllable Test (NST) across the two subject groups differed in both degree and type of error. Listeners in the group with poorer NST performance always demonstrated greater difficulty with selected low-frequency and high-frequency syllables than did listeners in the group with better NST performance. Overall, the NST was sensitive to differences in consonant-recognition ability for normal-hearing and hearing-impaired listeners.

Effects of Power on Mental Rotation and Emotion Recognition in Women.

PubMed

Nissan, Tali; Shapira, Oren; Liberman, Nira

2015-10-01

Based on construal-level theory (CLT) and its view of power as an instance of social distance, we predicted that high, relative to low power would enhance women's mental-rotation performance and impede their emotion-recognition performance. The predicted effects of power emerged both when it was manipulated via a recall priming task (Study 1) and environmental cues (Studies 2 and 3). Studies 3 and 4 found evidence for mediation by construal level of the effect of power on emotion recognition but not on mental rotation. We discuss potential mediating mechanisms for these effects based on both the social distance/construal level and the approach/inhibition views of power. We also discuss implications for optimizing performance on mental rotation and emotion recognition in everyday life. © 2015 by the Society for Personality and Social Psychology, Inc.

Obligatory and facultative brain regions for voice-identity recognition

PubMed Central

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Abstract Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is only a facultative component of voice-identity recognition in situations where additional face-identity processing is required. PMID:29228111

Obligatory and facultative brain regions for voice-identity recognition.

PubMed

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is only a facultative component of voice-identity recognition in situations where additional face-identity processing is required. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Impact of Childhood Maltreatment on the Recognition of Facial Expressions of Emotions.

PubMed

Ardizzi, Martina; Martini, Francesca; Umiltà, Maria Alessandra; Evangelista, Valentina; Ravera, Roberto; Gallese, Vittorio

2015-01-01

The development of the explicit recognition of facial expressions of emotions can be affected by childhood maltreatment experiences. A previous study demonstrated the existence of an explicit recognition bias for angry facial expressions among a population of adolescent Sierra Leonean street-boys exposed to high levels of maltreatment. In the present study, the recognition bias for angry facial expressions was investigated in a younger population of street-children and age-matched controls. Participants performed a forced-choice facial expressions recognition task. Recognition bias was measured as participants' tendency to over-attribute anger label to other negative facial expressions. Participants' heart rate was assessed and related to their behavioral performance, as index of their stress-related physiological responses. Results demonstrated the presence of a recognition bias for angry facial expressions among street-children, also pinpointing a similar, although significantly less pronounced, tendency among controls. Participants' performance was controlled for age, cognitive and educational levels and for naming skills. None of these variables influenced the recognition bias for angry facial expressions. Differently, a significant effect of heart rate on participants' tendency to use anger label was evidenced. Taken together, these results suggest that childhood exposure to maltreatment experiences amplifies children's "pre-existing bias" for anger labeling in forced-choice emotion recognition task. Moreover, they strengthen the thesis according to which the recognition bias for angry facial expressions is a manifestation of a functional adaptive mechanism that tunes victim's perceptive and attentive focus on salient environmental social stimuli.

Impact of Childhood Maltreatment on the Recognition of Facial Expressions of Emotions

PubMed Central

Ardizzi, Martina; Martini, Francesca; Umiltà, Maria Alessandra; Evangelista, Valentina; Ravera, Roberto; Gallese, Vittorio

2015-01-01

The development of the explicit recognition of facial expressions of emotions can be affected by childhood maltreatment experiences. A previous study demonstrated the existence of an explicit recognition bias for angry facial expressions among a population of adolescent Sierra Leonean street-boys exposed to high levels of maltreatment. In the present study, the recognition bias for angry facial expressions was investigated in a younger population of street-children and age-matched controls. Participants performed a forced-choice facial expressions recognition task. Recognition bias was measured as participants’ tendency to over-attribute anger label to other negative facial expressions. Participants’ heart rate was assessed and related to their behavioral performance, as index of their stress-related physiological responses. Results demonstrated the presence of a recognition bias for angry facial expressions among street-children, also pinpointing a similar, although significantly less pronounced, tendency among controls. Participants’ performance was controlled for age, cognitive and educational levels and for naming skills. None of these variables influenced the recognition bias for angry facial expressions. Differently, a significant effect of heart rate on participants’ tendency to use anger label was evidenced. Taken together, these results suggest that childhood exposure to maltreatment experiences amplifies children’s “pre-existing bias” for anger labeling in forced-choice emotion recognition task. Moreover, they strengthen the thesis according to which the recognition bias for angry facial expressions is a manifestation of a functional adaptive mechanism that tunes victim’s perceptive and attentive focus on salient environmental social stimuli. PMID:26509890

Recognizing "me" benefits "we": Investigating the positive spillover effects of formal individual recognition in teams.

PubMed

Li, Ning; Zheng, Xiaoming; Harris, T Brad; Liu, Xin; Kirkman, Bradley L

2016-07-01

Many organizations use formal recognition programs (e.g., "employee of the month") as a way to publically acknowledge an individual employee's outstanding performance and motivate continued high performance. However, it remains unclear whether emphasizing individual achievement in a team context is beneficial or detrimental for recipients' teammates and, by extension, the team as a whole. Drawing on a social influence perspective, we examine potential spillover effects of individual formal recognition programs in teams. We hypothesize that a single team member's recognition will produce positive spillover effects on other team members' performance, as well as overall team performance, via social influence processes, especially when the award recipient is located in a central position in a team. Findings from 2 lab experiments of 24 teams and 40 teams (Study 1 and Study 2, respectively) and a field experiment of 52 manufacturing teams (Study 3) reveal that formally recognizing a team member leads to positive changes in her/his teammates' individual and collective performance. Thus, formal social recognition programs can potentially provide a motivational effect beyond individual recipients. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Swedish Hayling task, and its relation to working memory, verbal ability, and speech-recognition-in-noise.

PubMed

Stenbäck, Victoria; Hällgren, Mathias; Lyxell, Björn; Larsby, Birgitta

2015-06-01

Cognitive functions and speech-recognition-in-noise were evaluated with a cognitive test battery, assessing response inhibition using the Hayling task, working memory capacity (WMC) and verbal information processing, and an auditory test of speech recognition. The cognitive tests were performed in silence whereas the speech recognition task was presented in noise. Thirty young normally-hearing individuals participated in the study. The aim of the study was to investigate one executive function, response inhibition, and whether it is related to individual working memory capacity (WMC), and how speech-recognition-in-noise relates to WMC and inhibitory control. The results showed a significant difference between initiation and response inhibition, suggesting that the Hayling task taps cognitive activity responsible for executive control. Our findings also suggest that high verbal ability was associated with better performance in the Hayling task. We also present findings suggesting that individuals who perform well on tasks involving response inhibition, and WMC, also perform well on a speech-in-noise task. Our findings indicate that capacity to resist semantic interference can be used to predict performance on speech-in-noise tasks. © 2015 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Units of Distinction: Creating a Blueprint for Recognition of High-Performing Medical-Surgical Nursing Units.

PubMed

Jeffery, Alvin D; Mosier, Sammie; Baker, Allison; Korwek, Kimberly; Borum, Cindy; Englebright, Jane

2018-02-01

Hospital medical-surgical (M/S) nursing units are responsible for up to 28 million encounters annually, yet receive little attention from professional organizations and national initiatives targeted to improve quality and performance. We sought to develop a framework recognizing high-performing units within our large hospital system. This was a retrospective data analysis of M/S units throughout a 168-hospital system. Measures represented patient experience, employee engagement, staff scheduling, nursing-sensitive patient outcomes, professional practices, and clinical process measures. Four hundred ninety units from 129 hospitals contributed information to test the framework. A manual scoring system identified the top 5% and recognized them as a "Unit of Distinction." Secondary analyses with machine learning provided validation of the proposed framework. Similar to external recognition programs, this framework and process provide a holistic evaluation useful for meaningful recognition and lay the groundwork for benchmarking in improvement efforts.

Pictures, images, and recollective experience.

PubMed

Dewhurst, S A; Conway, M A

1994-09-01

Five experiments investigated the influence of picture processing on recollective experience in recognition memory. Subjects studied items that differed in visual or imaginal detail, such as pictures versus words and high-imageability versus low-imageability words, and performed orienting tasks that directed processing either toward a stimulus as a word or toward a stimulus as a picture or image. Standard effects of imageability (e.g., the picture superiority effect and memory advantages following imagery) were obtained only in recognition judgments that featured recollective experience and were eliminated or reversed when recognition was not accompanied by recollective experience. It is proposed that conscious recollective experience in recognition memory is cued by attributes of retrieved memories such as sensory-perceptual attributes and records of cognitive operations performed at encoding.

Fast and accurate face recognition based on image compression

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2017-05-01

Image compression is desired for many image-related applications especially for network-based applications with bandwidth and storage constraints. The face recognition community typical reports concentrate on the maximal compression rate that would not decrease the recognition accuracy. In general, the wavelet-based face recognition methods such as EBGM (elastic bunch graph matching) and FPB (face pattern byte) are of high performance but run slowly due to their high computation demands. The PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) algorithms run fast but perform poorly in face recognition. In this paper, we propose a novel face recognition method based on standard image compression algorithm, which is termed as compression-based (CPB) face recognition. First, all gallery images are compressed by the selected compression algorithm. Second, a mixed image is formed with the probe and gallery images and then compressed. Third, a composite compression ratio (CCR) is computed with three compression ratios calculated from: probe, gallery and mixed images. Finally, the CCR values are compared and the largest CCR corresponds to the matched face. The time cost of each face matching is about the time of compressing the mixed face image. We tested the proposed CPB method on the "ASUMSS face database" (visible and thermal images) from 105 subjects. The face recognition accuracy with visible images is 94.76% when using JPEG compression. On the same face dataset, the accuracy of FPB algorithm was reported as 91.43%. The JPEG-compressionbased (JPEG-CPB) face recognition is standard and fast, which may be integrated into a real-time imaging device.

Superior Recognition Performance for Happy Masked and Unmasked Faces in Both Younger and Older Adults

PubMed Central

Svärd, Joakim; Wiens, Stefan; Fischer, Håkan

2012-01-01

In the aging literature it has been shown that even though emotion recognition performance decreases with age, the decrease is less for happiness than other facial expressions. Studies in younger adults have also revealed that happy faces are more strongly attended to and better recognized than other emotional facial expressions. Thus, there might be a more age independent happy face advantage in facial expression recognition. By using a backward masking paradigm and varying stimulus onset asynchronies (17–267 ms) the temporal development of a happy face advantage, on a continuum from low to high levels of visibility, was examined in younger and older adults. Results showed that across age groups, recognition performance for happy faces was better than for neutral and fearful faces at durations longer than 50 ms. Importantly, the results showed a happy face advantage already during early processing of emotional faces in both younger and older adults. This advantage is discussed in terms of processing of salient perceptual features and elaborative processing of the happy face. We also investigate the combined effect of age and neuroticism on emotional face processing. The rationale was previous findings of age-related differences in physiological arousal to emotional pictures and a relation between arousal and neuroticism. Across all durations, there was an interaction between age and neuroticism, showing that being high in neuroticism might be disadvantageous for younger, but not older adults’ emotion recognition performance during arousal enhancing tasks. These results indicate that there is a relation between aging, neuroticism, and performance, potentially related to physiological arousal. PMID:23226135

An audiovisual emotion recognition system

NASA Astrophysics Data System (ADS)

Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

2007-12-01

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

Improving the recognition of fingerprint biometric system using enhanced image fusion

NASA Astrophysics Data System (ADS)

Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

2010-04-01

Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition.

PubMed

Zhang, Yifan; Gao, Xunzhang; Peng, Xuan; Ye, Jiaqi; Li, Xiang

2018-05-16

The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

Modeling the effect of channel number and interaction on consonant recognition in a cochlear implant peak-picking strategy.

PubMed

Verschuur, Carl

2009-03-01

Difficulties in speech recognition experienced by cochlear implant users may be attributed both to information loss caused by signal processing and to information loss associated with the interface between the electrode array and auditory nervous system, including cross-channel interaction. The objective of the work reported here was to attempt to partial out the relative contribution of these different factors to consonant recognition. This was achieved by comparing patterns of consonant feature recognition as a function of channel number and presence/absence of background noise in users of the Nucleus 24 device with normal hearing subjects listening to acoustic models that mimicked processing of that device. Additionally, in the acoustic model experiment, a simulation of cross-channel spread of excitation, or "channel interaction," was varied. Results showed that acoustic model experiments were highly correlated with patterns of performance in better-performing cochlear implant users. Deficits to consonant recognition in this subgroup could be attributed to cochlear implant processing, whereas channel interaction played a much smaller role in determining performance errors. The study also showed that large changes to channel number in the Advanced Combination Encoder signal processing strategy led to no substantial changes in performance.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

PubMed

Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

The low-frequency encoding disadvantage: Word frequency affects processing demands.

PubMed

Diana, Rachel A; Reder, Lynne M

2006-07-01

Low-frequency words produce more hits and fewer false alarms than high-frequency words in a recognition task. The low-frequency hit rate advantage has sometimes been attributed to processes that operate during the recognition test (e.g., L. M. Reder et al., 2000). When tasks other than recognition, such as recall, cued recall, or associative recognition, are used, the effects seem to contradict a low-frequency advantage in memory. Four experiments are presented to support the claim that in addition to the advantage of low-frequency words at retrieval, there is a low-frequency disadvantage during encoding. That is, low-frequency words require more processing resources to be encoded episodically than high-frequency words. Under encoding conditions in which processing resources are limited, low-frequency words show a larger decrement in recognition than high-frequency words. Also, studying items (pictures and words of varying frequencies) along with low-frequency words reduces performance for those stimuli. Copyright 2006 APA, all rights reserved.

Emotion-attention interactions in recognition memory for distractor faces.

PubMed

Srinivasan, Narayanan; Gupta, Rashmi

2010-04-01

Effective filtering of distractor information has been shown to be dependent on perceptual load. Given the salience of emotional information and the presence of emotion-attention interactions, we wanted to explore the recognition memory for emotional distractors especially as a function of focused attention and distributed attention by manipulating load and the spatial spread of attention. We performed two experiments to study emotion-attention interactions by measuring recognition memory performance for distractor neutral and emotional faces. Participants performed a color discrimination task (low-load) or letter identification task (high-load) with a letter string display in Experiment 1 and a high-load letter identification task with letters presented in a circular array in Experiment 2. The stimuli were presented against a distractor face background. The recognition memory results show that happy faces were recognized better than sad faces under conditions of less focused or distributed attention. When attention is more spatially focused, sad faces were recognized better than happy faces. The study provides evidence for emotion-attention interactions in which specific emotional information like sad or happy is associated with focused or distributed attention respectively. Distractor processing with emotional information also has implications for theories of attention. Copyright 2010 APA, all rights reserved.

Continuous multiword recognition performance of young and elderly listeners in ambient noise

NASA Astrophysics Data System (ADS)

Sato, Hiroshi

2005-09-01

Hearing threshold shift due to aging is known as a dominant factor to degrade speech recognition performance in noisy conditions. On the other hand, cognitive factors of aging-relating speech recognition performance in various speech-to-noise conditions are not well established. In this study, two kinds of speech test were performed to examine how working memory load relates to speech recognition performance. One is word recognition test with high-familiarity, four-syllable Japanese words (single-word test). In this test, each word was presented to listeners; the listeners were asked to write the word down on paper with enough time to answer. In the other test, five continuous word were presented to listeners and listeners were asked to write the word down after just five words were presented (multiword test). Both tests were done in various speech-to-noise ratios under 50-dBA Hoth spectrum noise with more than 50 young and elderly subjects. The results of two experiments suggest that (1) Hearing level is related to scores of both tests. (2) Scores of single-word test are well correlated with those of multiword test. (3) Scores of multiword test are not improved as speech-to-noise ratio improves in the condition where scores of single-word test reach their ceiling.

Target recognition of ladar range images using even-order Zernike moments.

PubMed

Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi

2012-11-01

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

PubMed

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pattern Perception and Pictures for the Blind

ERIC Educational Resources Information Center

Heller, Morton A.; McCarthy, Melissa; Clark, Ashley

2005-01-01

This article reviews recent research on perception of tangible pictures in sighted and blind people. Haptic picture naming accuracy is dependent upon familiarity and access to semantic memory, just as in visual recognition. Performance is high when haptic picture recognition tasks do not depend upon semantic memory. Viewpoint matters for the ease…

Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung

2010-06-01

Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.

The MIT Summit Speech Recognition System: A Progress Report

DTIC Science & Technology

1989-01-01

understanding of the human communication process. Despite recent development of some speech recognition systems with high accuracy, the performance of such...over the past four decades on human communication , in the hope that such systems will one day have a performance approaching that of humans. We are...optimize its use. Third, the system must have a stochastic component to deal with the present state of ignorance in our understanding of the human

Re-examination of the role of the human acoustic stapedius reflex

NASA Astrophysics Data System (ADS)

Phillips, Dennis P.; Stuart, Andrew; Carpenter, Michael

2002-05-01

The ``rollover'' seen in the word recognition performance scores of patients with Bell's palsy (facial nerve paralysis) has historically been taken as an indicator of the role of the stapedius reflex in the protection from upward spread of masking. Bell's palsy, however, may be a polyneuropathy, so it is not clear that the poor word recognition performance at high levels is necessarily attributable specifically to impaired facial nerve function. The present article reports two new experiments that probe whether an isolated impairment of the stapedius reflex can produce rollover in word recognition performance-intensity functions. In experiment 1, performance-intensity functions for monosyllabic speech materials were obtained from ten normal listeners under two listening conditions: normal and low-frequency augmented to offset the effects of the stapedius reflex on the transmission of low-frequency vibrations to the cochlea. There was no effect of the spectral augmentation on word recognition for stimulus levels up to 107 dB SPL. In experiment 2, six patients who had undergone stapedectomy were tested for rollover using performance-intensity functions. None of the patients showed rollover in their performance-intensity functions, even at stimulus levels in excess of 100 dB HL. These data suggest that if the stapedius reflex has a role in protection from upward spread of masking, then this role is inconsequential for word recognition in quiet.

Physical environment virtualization for human activities recognition

NASA Astrophysics Data System (ADS)

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2015-05-01

Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

Recognition Imaging of Acetylated Chromatin Using a DNA Aptamer

PubMed Central

Lin, Liyun; Fu, Qiang; Williams, Berea A.R.; Azzaz, Abdelhamid M.; Shogren-Knaak, Michael A.; Chaput, John C.; Lindsay, Stuart

2009-01-01

Histone acetylation plays an important role in the regulation of gene expression. A DNA aptamer generated by in vitro selection to be highly specific for histone H4 protein acetylated at lysine 16 was used as a recognition element for atomic force microscopy-based recognition imaging of synthetic nucleosomal arrays with precisely controlled acetylation. The aptamer proved to be reasonably specific at recognizing acetylated histones, with recognition efficiencies of 60% on-target and 12% off-target. Though this selectivity is much poorer than the >2000:1 equilibrium specificity of the aptamer, it is a large improvement on the performance of a ChIP-quality antibody, which is not selective at all in this application, and it should permit high-fidelity recognition with repeated imaging. The ability to image the precise location of posttranslational modifications may permit nanometer-scale investigation of their effect on chromatin structure. PMID:19751687

Interpersonal Values and Academic Performance Related to Delinquent Behaviors

PubMed Central

Molero Jurado, María Del Mar; Pérez Fuentes, María Del Carmen; Luque De La Rosa, Antonio; Martos Martínez, África; Barragán Martín, Ana Belén; Simón Márquez, María del Mar

2016-01-01

The present study analyzes the relation between delinquent behaviors, interpersonal values, and academic performance. It also analyzes the possible protective function of interpersonal values against delinquent behaviors. The Interpersonal Values Questionnaire (IVQ) was used to assess interpersonal values, and the Antisocial-Delinquent Behaviors Questionnaire (A-D) was employed to assess antisocial behaviors. The sample was made up of 885 students of Compulsory Secondary Education, aged from 14 to 17 years. The results show that individuals who fail a subject as well as those who repeat a course present higher means in delinquent behaviors. Repeaters present higher means in the values of recognition and leadership, and non-repeaters in the value stimulation, whereas students who do not fail obtain higher scores in the value benevolence. Students with high levels of recognition, independence, and leadership, as well as students with low levels of conformity and benevolence display significantly higher levels of delinquent behaviors. Lastly, the probability of presenting a high level of delinquent behaviors is greater in individuals with: high independence, high leadership, high recognition, low benevolence, and low conformity. PMID:27799914

Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

PubMed

Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

2013-11-01

Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. Copyright © 2013 Elsevier Inc. All rights reserved.

Potential Benefits of an Integrated Electric-Acoustic Sound Processor with Children: A Preliminary Report.

PubMed

Wolfe, Jace; Neumann, Sara; Schafer, Erin; Marsh, Megan; Wood, Mark; Baker, R Stanley

2017-02-01

A number of published studies have demonstrated the benefits of electric-acoustic stimulation (EAS) over conventional electric stimulation for adults with functional low-frequency acoustic hearing and severe-to-profound high-frequency hearing loss. These benefits potentially include better speech recognition in quiet and in noise, better localization, improvements in sound quality, better music appreciation and aptitude, and better pitch recognition. There is, however, a paucity of published reports describing the potential benefits and limitations of EAS for children with functional low-frequency acoustic hearing and severe-to-profound high-frequency hearing loss. The objective of this study was to explore the potential benefits of EAS for children. A repeated measures design was used to evaluate performance differences obtained with EAS stimulation versus acoustic- and electric-only stimulation. Seven users of Cochlear Nucleus Hybrid, Nucleus 24 Freedom, CI512, and CI422 implants were included in the study. Sentence recognition (assayed using the pediatric version of the AzBio sentence recognition test) was evaluated in quiet and at three fixed signal-to-noise ratios (SNR) (0, +5, and +10 dB). Functional hearing performance was also evaluated with the use of questionnaires, including the comparative version of the Speech, Spatial, and Qualities, the Listening Inventory for Education Revised, and the Children's Home Inventory for Listening Difficulties. Speech recognition in noise was typically better with EAS compared to participants' performance with acoustic- and electric-only stimulation, particularly when evaluated at the less favorable SNR. Additionally, in real-world situations, children generally preferred to use EAS compared to electric-only stimulation. Also, the participants' classroom teachers observed better hearing performance in the classroom with the use of EAS. Use of EAS provided better speech recognition in quiet and in noise when compared to performance obtained with use of acoustic- and electric-only stimulation, and children responded favorably to the use of EAS implemented in an integrated sound processor for real-world use. American Academy of Audiology

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

NASA Astrophysics Data System (ADS)

Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

2018-04-01

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Clinical experience with the words-in-noise test on 3430 veterans: comparisons with pure-tone thresholds and word recognition in quiet.

PubMed

Wilson, Richard H

2011-01-01

Since the 1940s, measures of pure-tone sensitivity and speech recognition in quiet have been vital components of the audiologic evaluation. Although early investigators urged that speech recognition in noise also should be a component of the audiologic evaluation, only recently has this suggestion started to become a reality. This report focuses on the Words-in-Noise (WIN) Test, which evaluates word recognition in multitalker babble at seven signal-to-noise ratios and uses the 50% correct point (in dB SNR) calculated with the Spearman-Kärber equation as the primary metric. The WIN was developed and validated in a series of 12 laboratory studies. The current study examined the effectiveness of the WIN materials for measuring the word-recognition performance of patients in a typical clinical setting. To examine the relations among three audiometric measures including pure-tone thresholds, word-recognition performances in quiet, and word-recognition performances in multitalker babble for veterans seeking remediation for their hearing loss. Retrospective, descriptive. The participants were 3430 veterans who for the most part were evaluated consecutively in the Audiology Clinic at the VA Medical Center, Mountain Home, Tennessee. The mean age was 62.3 yr (SD = 12.8 yr). The data were collected in the course of a 60 min routine audiologic evaluation. A history, otoscopy, and aural-acoustic immittance measures also were included in the clinic protocol but were not evaluated in this report. Overall, the 1000-8000 Hz thresholds were significantly lower (better) in the right ear (RE) than in the left ear (LE). There was a direct relation between age and the pure-tone thresholds, with greater change across age in the high frequencies than in the low frequencies. Notched audiograms at 4000 Hz were observed in at least one ear in 41% of the participants with more unilateral than bilateral notches. Normal pure-tone thresholds (≤20 dB HL) were obtained from 6% of the participants. Maximum performance on the Northwestern University Auditory Test No. 6 (NU-6) in quiet was ≥90% correct by 50% of the participants, with an additional 20% performing at ≥80% correct; the RE performed 1-3% better than the LE. Of the 3291 who completed the WIN on both ears, only 7% exhibited normal performance (50% correct point of ≤6 dB SNR). Overall, WIN performance was significantly better in the RE (mean = 13.3 dB SNR) than in the LE (mean = 13.8 dB SNR). Recognition performance on both the NU-6 and the WIN decreased as a function of both pure-tone hearing loss and age. There was a stronger relation between the high-frequency pure-tone average (1000, 2000, and 4000 Hz) and the WIN than between the pure-tone average (500, 1000, and 2000 Hz) and the WIN. The results on the WIN from both the previous laboratory studies and the current clinical study indicate that the WIN is an appropriate clinic instrument to assess word-recognition performance in background noise. Recognition performance on a speech-in-quiet task does not predict performance on a speech-in-noise task, as the two tasks reflect different domains of auditory function. Experience with the WIN indicates that word-in-noise tasks should be considered the "stress test" for auditory function. American Academy of Audiology.

Formal implementation of a performance evaluation model for the face recognition system.

PubMed

Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

2008-01-01

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Logo recognition using alpha-rooted phase correlation in the radon transform domain

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2009-08-01

Alpha-rooted phase correlation (ARPC) is a recently-developed variant of classical phase correlation that includes a Fourier domain image enhancement operation. ARPC combines classical phase correlation with alpha-rooting to provide tunable image enhancement. The alpha-rooting parameters may be adjusted to provide a tradeoff between height and width of the ARPC main lobe. A high narrow main lobe peak provides high matching accuracy for aligned images, but reduced matching performance for misaligned logos. A lower, wider peak trades matching accuracy on aligned logos, for improved matching performance on misaligned imagery. Previously, we developed ARPC and used it in the spatial domain for logo recognition as part of an overall automated document analysis problem. However, spatial domain ARPC performance can be sensitive to logo misalignments, including rotational misalignment. In this paper we use ARPC as a match metric in the radon transform domain for logo recognition. In the radon transform domain, rotational misalignments correspond to translations in the radon transform angle parameter. These translations are captured by ARPC, thereby producing rotation-invariant logo matching. In the paper, we first present an overview of ARPC, and then describe the logo matching algorithm. We present numerical performance results demonstrating matching tolerance to rotational misalignments. We demonstrate robustness of the radon transform domain rotation estimation to noise. We present logo verification and recognition performance results using the proposed approach on a public domain logo database. We compare performance results to performance obtained using spatial domain ARPC, and state-of-the-art SURF features, for logos in salt-and-pepper noise.

Functional differences among those high and low on a trait measure of psychopathy.

PubMed

Gordon, Heather L; Baird, Abigail A; End, Alison

2004-10-01

It has been established that individuals who score high on measures of psychopathy demonstrate difficulty when performing tasks requiring the interpretation of other's emotional states. The aim of this study was to elucidate the relation of emotion and cognition to individual differences on a standard psychopathy personality inventory (PPI) among a nonpsychiatric population. Twenty participants completed the PPI. Following survey completion, a mean split of their scores on the emotional-interpersonal factor was performed, and participants were placed into a high or low group. Functional magnetic resonance imaging data were collected while participants performed a recognition task that required attention be given to either the affect or identity of target stimuli. No significant behavioral differences were found. In response to the affect recognition task, significant differences between high- and low-scoring subjects were observed in several subregions of the frontal cortex, as well as the amygdala. No significant differences were found between the groups in response to the identity recognition condition. Results indicate that participants scoring high on the PPI, although not behaviorally distinct, demonstrate a significantly different pattern of neural activity (as measured by blood oxygen level-dependent contrast)in response to tasks that require affective processing. The results suggest a unique neural signature associated with personality differences in a nonpsychiatric population.

Schizotypy and impaired basic face recognition? Another non-confirmatory study.

PubMed

Bell, Vaughan; Halligan, Peter

2015-12-01

Although schizotypy has been found to be reliably associated with a reduced recognition of facial affect, the few studies that have tested the association between basic face recognition abilities and schizotypy have found mixed results. This study formally tested the association in a large non-clinical sample with established neurological measures of face recognition. Two hundred and twenty-seven participants completed the Oxford-Liverpool Inventory of Feelings and Experiences schizotypy scale and completed the Famous Faces Test and the Cardiff Repeated Recognition Test for Faces. No association between any schizotypal dimension and performance on either of the facial recognition and learning tests was found. The null results can be accepted with a high degree of confidence. Further additional evidence is provided for a lack of association between schizotypy and basic face recognition deficits. © 2014 Wiley Publishing Asia Pty Ltd.

New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2013-04-01

Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

A model of attention-guided visual perception and recognition.

PubMed

Rybak, I A; Gusakova, V I; Golovan, A V; Podladchikova, L N; Shevtsova, N A

1998-08-01

A model of visual perception and recognition is described. The model contains: (i) a low-level subsystem which performs both a fovea-like transformation and detection of primary features (edges), and (ii) a high-level subsystem which includes separated 'what' (sensory memory) and 'where' (motor memory) structures. Image recognition occurs during the execution of a 'behavioral recognition program' formed during the primary viewing of the image. The recognition program contains both programmed attention window movements (stored in the motor memory) and predicted image fragments (stored in the sensory memory) for each consecutive fixation. The model shows the ability to recognize complex images (e.g. faces) invariantly with respect to shift, rotation and scale.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Examination of the neighborhood activation theory in normal and hearing-impaired listeners.

PubMed

Dirks, D D; Takayanagi, S; Moshfegh, A; Noffsinger, P D; Fausti, S A

2001-02-01

Experiments were conducted to examine the effects of lexical information on word recognition among normal hearing listeners and individuals with sensorineural hearing loss. The lexical factors of interest were incorporated in the Neighborhood Activation Model (NAM). Central to this model is the concept that words are recognized relationally in the context of other phonemically similar words. NAM suggests that words in the mental lexicon are organized into similarity neighborhoods and the listener is required to select the target word from competing lexical items. Two structural characteristics of similarity neighborhoods that influence word recognition have been identified; "neighborhood density" or the number of phonemically similar words (neighbors) for a particular target item and "neighborhood frequency" or the average frequency of occurrence of all the items within a neighborhood. A third lexical factor, "word frequency" or the frequency of occurrence of a target word in the language, is assumed to optimize the word recognition process by biasing the system toward choosing a high frequency over a low frequency word. Three experiments were performed. In the initial experiments, word recognition for consonant-vowel-consonant (CVC) monosyllables was assessed in young normal hearing listeners by systematically partitioning the items into the eight possible lexical conditions that could be created by two levels of the three lexical factors, word frequency (high and low), neighborhood density (high and low), and average neighborhood frequency (high and low). Neighborhood structure and word frequency were estimated computationally using a large, on-line lexicon-based Webster's Pocket Dictionary. From this program 400 highly familiar, monosyllables were selected and partitioned into eight orthogonal lexical groups (50 words/group). The 400 words were presented randomly to normal hearing listeners in speech-shaped noise (Experiment 1) and "in quiet" (Experiment 2) as well as to an elderly group of listeners with sensorineural hearing loss in the speech-shaped noise (Experiment 3). The results of three experiments verified predictions of NAM in both normal hearing and hearing-impaired listeners. In each experiment, words from low density neighborhoods were recognized more accurately than those from high density neighborhoods. The presence of high frequency neighbors (average neighborhood frequency) produced poorer recognition performance than comparable conditions with low frequency neighbors. Word frequency was found to have a highly significant effect on word recognition. Lexical conditions with high word frequencies produced higher performance scores than conditions with low frequency words. The results supported the basic tenets of NAM theory and identified both neighborhood structural properties and word frequency as significant lexical factors affecting word recognition when listening in noise and "in quiet." The results of the third experiment permit extension of NAM theory to individuals with sensorineural hearing loss. Future development of speech recognition tests should allow for the effects of higher level cognitive (lexical) factors on lower level phonemic processing.

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

NASA Astrophysics Data System (ADS)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

Employee Retention and Performance Improvement in High-Tech Companies.

ERIC Educational Resources Information Center

Ware, B. Lynn

2001-01-01

Considers the benefits of employee retention and performance improvement in high technology, new economy companies. Discusses attracting and retaining top talent in information technology companies; targeted recruiting and hiring; employee achievement; learning and professional growth; recognition; nurturing careers; team collaboration; the TALENT…

Pc-based car license plate reading

NASA Astrophysics Data System (ADS)

Tanabe, Katsuyoshi; Marubayashi, Eisaku; Kawashima, Harumi; Nakanishi, Tadashi; Shio, Akio

1994-03-01

A PC-based car license plate recognition system has been developed. The system recognizes Chinese characters and Japanese phonetic hiragana characters as well as six digits on Japanese license plates. The system consists of a CCD camera, vehicle sensors, a strobe unit, a monitoring center, and an i486-based PC. The PC includes in its extension slots: a vehicle detector board, a strobe emitter board, and an image grabber board. When a passing vehicle is detected by the vehicle sensors, the strobe emits a pulse of light. The light pulse is synchronized with the time the vehicle image is frozen on an image grabber board. The recognition process is composed of three steps: image thresholding, character region extraction, and matching-based character recognition. The recognition software can handle obscured characters. Experimental results for hundreds of outdoor images showed high recognition performance within relatively short performance times. The results confirmed that the system is applicable to a wide variety of applications such as automatic vehicle identification and travel time measurement.

Illumination-invariant hand gesture recognition

NASA Astrophysics Data System (ADS)

Mendoza-Morales, América I.; Miramontes-Jaramillo, Daniel; Kober, Vitaly

2015-09-01

In recent years, human-computer interaction (HCI) has received a lot of interest in industry and science because it provides new ways to interact with modern devices through voice, body, and facial/hand gestures. The application range of the HCI is from easy control of home appliances to entertainment. Hand gesture recognition is a particularly interesting problem because the shape and movement of hands usually are complex and flexible to be able to codify many different signs. In this work we propose a three step algorithm: first, detection of hands in the current frame is carried out; second, hand tracking across the video sequence is performed; finally, robust recognition of gestures across subsequent frames is made. Recognition rate highly depends on non-uniform illumination of the scene and occlusion of hands. In order to overcome these issues we use two Microsoft Kinect devices utilizing combined information from RGB and infrared sensors. The algorithm performance is tested in terms of recognition rate and processing time.

Evidence for a confidence-accuracy relationship in memory for same- and cross-race faces.

PubMed

Nguyen, Thao B; Pezdek, Kathy; Wixted, John T

2017-12-01

Discrimination accuracy is usually higher for same- than for cross-race faces, a phenomenon known as the cross-race effect (CRE). According to prior research, the CRE occurs because memories for same- and cross-race faces rely on qualitatively different processes. However, according to a continuous dual-process model of recognition memory, memories that rely on qualitatively different processes do not differ in recognition accuracy when confidence is equated. Thus, although there are differences in overall same- and cross-race discrimination accuracy, confidence-specific accuracy (i.e., recognition accuracy at a particular level of confidence) may not differ. We analysed datasets from four recognition memory studies on same- and cross-race faces to test this hypothesis. Confidence ratings reliably predicted recognition accuracy when performance was above chance levels (Experiments 1, 2, and 3) but not when performance was at chance levels (Experiment 4). Furthermore, at each level of confidence, confidence-specific accuracy for same- and cross-race faces did not significantly differ when overall performance was above chance levels (Experiments 1, 2, and 3) but significantly differed when overall performance was at chance levels (Experiment 4). Thus, under certain conditions, high-confidence same-race and cross-race identifications may be equally reliable.

Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors

PubMed Central

Pham, Tuyen Danh; Park, Young Ho; Nguyen, Dat Tien; Kwon, Seung Yong; Park, Kang Ryoung

2015-01-01

Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands. PMID:26184214

Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors.

PubMed

Pham, Tuyen Danh; Park, Young Ho; Nguyen, Dat Tien; Kwon, Seung Yong; Park, Kang Ryoung

2015-07-13

Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

Outlining face processing skills of portrait artists: Perceptual experience with faces predicts performance.

PubMed

Devue, Christel; Barsics, Catherine

2016-10-01

Most humans seem to demonstrate astonishingly high levels of skill in face processing if one considers the sophisticated level of fine-tuned discrimination that face recognition requires. However, numerous studies now indicate that the ability to process faces is not as fundamental as once thought and that performance can range from despairingly poor to extraordinarily high across people. Here we studied people who are super specialists of faces, namely portrait artists, to examine how their specific visual experience with faces relates to a range of face processing skills (perceptual discrimination, short- and longer term recognition). Artists show better perceptual discrimination and, to some extent, recognition of newly learned faces than controls. They are also more accurate on other perceptual tasks (i.e., involving non-face stimuli or mental rotation). By contrast, artists do not display an advantage compared to controls on longer term face recognition (i.e., famous faces) nor on person recognition from other sensorial modalities (i.e., voices). Finally, the face inversion effect exists in artists and controls and is not modulated by artistic practice. Advantages in face processing for artists thus seem to closely mirror perceptual and visual short term memory skills involved in portraiture. Copyright © 2016 Elsevier Ltd. All rights reserved.

Noise-immune multisensor transduction of speech

NASA Astrophysics Data System (ADS)

Viswanathan, Vishu R.; Henry, Claudia M.; Derr, Alan G.; Roucos, Salim; Schwartz, Richard M.

1986-08-01

Two types of configurations of multiple sensors were developed, tested and evaluated in speech recognition application for robust performance in high levels of acoustic background noise: One type combines the individual sensor signals to provide a single speech signal input, and the other provides several parallel inputs. For single-input systems, several configurations of multiple sensors were developed and tested. Results from formal speech intelligibility and quality tests in simulated fighter aircraft cockpit noise show that each of the two-sensor configurations tested outperforms the constituent individual sensors in high noise. Also presented are results comparing the performance of two-sensor configurations and individual sensors in speaker-dependent, isolated-word speech recognition tests performed using a commercial recognizer (Verbex 4000) in simulated fighter aircraft cockpit noise.

Recognition memory across the lifespan: the impact of word frequency and study-test interval on estimates of familiarity and recollection

PubMed Central

Meier, Beat; Rey-Mermet, Alodie; Rothen, Nicolas; Graf, Peter

2013-01-01

The goal of this study was to investigate recognition memory performance across the lifespan and to determine how estimates of recollection and familiarity contribute to performance. In each of three experiments, participants from five groups from 14 up to 85 years of age (children, young adults, middle-aged adults, young-old adults, and old-old adults) were presented with high- and low-frequency words in a study phase and were tested immediately afterwards and/or after a one day retention interval. The results showed that word frequency and retention interval affected recognition memory performance as well as estimates of recollection and familiarity. Across the lifespan, the trajectory of recognition memory followed an inverse u-shape function that was neither affected by word frequency nor by retention interval. The trajectory of estimates of recollection also followed an inverse u-shape function, and was especially pronounced for low-frequency words. In contrast, estimates of familiarity did not differ across the lifespan. The results indicate that age differences in recognition memory are mainly due to differences in processes related to recollection while the contribution of familiarity-based processes seems to be age-invariant. PMID:24198796

Interpersonal value profiles and analysis of adolescent academic performance and social thinking

PubMed Central

Gázquez, José J.; Sainz, Jorge; Pérez-Fuentes, María del C.; Molero, María del M.; Soler, Francisco J.

2015-01-01

The purposes of this study were to identify interpersonal value profiles and find out whether there were any differences in academic performance and social thinking. The study sample was 885 high school students of whom 49.8% (N = 441) were boys and 50.2% (N = 444) were girls. The results show that students with low Benevolence and Conformity levels showed higher prevalence of failures and repeated the year more often. Furthermore, students with a high level of Recognition and Leadership and low Conformity and Benevolence are socially incompetent students. Intervention programs should to achieve high levels of kindness and consideration, respect for rules and generosity, and diminish the perception of recognition by others and exertion of authority. Thus, this study shows the values that must be worked on to improve students’ Academic Performance and social competence. PMID:25999891

Multisensory speech perception in autism spectrum disorder: From phoneme to whole-word perception.

PubMed

Stevenson, Ryan A; Baum, Sarah H; Segers, Magali; Ferber, Susanne; Barense, Morgan D; Wallace, Mark T

2017-07-01

Speech perception in noisy environments is boosted when a listener can see the speaker's mouth and integrate the auditory and visual speech information. Autistic children have a diminished capacity to integrate sensory information across modalities, which contributes to core symptoms of autism, such as impairments in social communication. We investigated the abilities of autistic and typically-developing (TD) children to integrate auditory and visual speech stimuli in various signal-to-noise ratios (SNR). Measurements of both whole-word and phoneme recognition were recorded. At the level of whole-word recognition, autistic children exhibited reduced performance in both the auditory and audiovisual modalities. Importantly, autistic children showed reduced behavioral benefit from multisensory integration with whole-word recognition, specifically at low SNRs. At the level of phoneme recognition, autistic children exhibited reduced performance relative to their TD peers in auditory, visual, and audiovisual modalities. However, and in contrast to their performance at the level of whole-word recognition, both autistic and TD children showed benefits from multisensory integration for phoneme recognition. In accordance with the principle of inverse effectiveness, both groups exhibited greater benefit at low SNRs relative to high SNRs. Thus, while autistic children showed typical multisensory benefits during phoneme recognition, these benefits did not translate to typical multisensory benefit of whole-word recognition in noisy environments. We hypothesize that sensory impairments in autistic children raise the SNR threshold needed to extract meaningful information from a given sensory input, resulting in subsequent failure to exhibit behavioral benefits from additional sensory information at the level of whole-word recognition. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1280-1290. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Automatic face recognition in HDR imaging

NASA Astrophysics Data System (ADS)

Pereira, Manuela; Moreno, Juan-Carlos; Proença, Hugo; Pinheiro, António M. G.

2014-05-01

The gaining popularity of the new High Dynamic Range (HDR) imaging systems is raising new privacy issues caused by the methods used for visualization. HDR images require tone mapping methods for an appropriate visualization on conventional and non-expensive LDR displays. These visualization methods might result in completely different visualization raising several issues on privacy intrusion. In fact, some visualization methods result in a perceptual recognition of the individuals, while others do not even show any identity. Although perceptual recognition might be possible, a natural question that can rise is how computer based recognition will perform using tone mapping generated images? In this paper, a study where automatic face recognition using sparse representation is tested with images that result from common tone mapping operators applied to HDR images. Its ability for the face identity recognition is described. Furthermore, typical LDR images are used for the face recognition training.

Response-related fMRI of veridical and false recognition of words.

PubMed

Heun, Reinhard; Jessen, Frank; Klose, Uwe; Erb, Michael; Granath, Dirk-Oliver; Grodd, Wolfgang

2004-02-01

Studies on the relation between local cerebral activation and retrieval success usually compared high and low performance conditions, and thus showed performance-related activation of different brain areas. Only a few studies directly compared signal intensities of different response categories during retrieval. During verbal recognition, we recently observed increased parieto-occipital activation related to false alarms. The present study intends to replicate and extend this observation by investigating common and differential activation by veridical and false recognition. Fifteen healthy volunteers performed a verbal recognition paradigm using 160 learned target and 160 new distractor words. The subjects had to indicate whether they had learned the word before or not. Echo-planar MRI of blood-oxygen-level-dependent signal changes was performed during this recognition task. Words were classified post hoc according to the subjects' responses, i.e. hits, false alarms, correct rejections and misses. Response-related fMRI-analysis was used to compare activation associated with the subjects' recognition success, i.e. signal intensities related to the presentation of words were compared by the above-mentioned four response types. During recognition, all word categories showed increased bilateral activation of the inferior frontal gyrus, the inferior temporal gyrus, the occipital lobe and the brainstem in comparison with the control condition. Hits and false alarms activated several areas including the left medial and lateral parieto-occipital cortex in comparison with subjectively unknown items, i.e. correct rejections and misses. Hits showed more pronounced activation in the medial, false alarms in the lateral parts of the left parieto-occipital cortex. Veridical and false recognition show common as well as different areas of cerebral activation in the left parieto-occipital lobe: increased activation of the medial parietal cortex by hits may correspond to true recognition, increased activation of the parieto-occipital cortex by false alarms may correspond to familiarity decisions. Further studies are needed to investigate the reasons for false decisions in healthy subjects and patients with memory problems.

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

PubMed

Lee, Yooyoung; Micheals, Ross J; Filliben, James J; Phillips, P Jonathon

2013-01-01

The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies

PubMed Central

Lee, Yooyoung; Micheals, Ross J; Filliben, James J; Phillips, P Jonathon

2013-01-01

The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST’s measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform. PMID:26401431

rTMS on left prefrontal cortex contributes to memories for positive emotional cues: a comparison between pictures and words.

PubMed

Balconi, M; Cobelli, C

2015-02-26

The present research explored the cortical correlates of emotional memories in response to words and pictures. Subjects' performance (Accuracy Index, AI; response times, RTs; RTs/AI) was considered when a repetitive Transcranial Magnetic Stimulation (rTMS) was applied on the left dorsolateral prefrontal cortex (LDLPFC). Specifically, the role of LDLPFC was tested by performing a memory task, in which old (previously encoded targets) and new (previously not encoded distractors) emotional pictures/words had to be recognized. Valence (positive vs. negative) and arousing power (high vs. low) of stimuli were also modulated. Moreover, subjective evaluation of emotional stimuli in terms of valence/arousal was explored. We found significant performance improving (higher AI, reduced RTs, improved general performance) in response to rTMS. This "better recognition effect" was only related to specific emotional features, that is positive high arousal pictures or words. Moreover no significant differences were found between stimulus categories. A direct relationship was also observed between subjective evaluation of emotional cues and memory performance when rTMS was applied to LDLPFC. Supported by valence and approach model of emotions, we supposed that a left lateralized prefrontal system may induce a better recognition of positive high arousal words, and that evaluation of emotional cue is related to prefrontal activation, affecting the recognition memories of emotions. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Effect of Stimulation Rate on Cochlear Implant Users’ Phoneme, Word and Sentence Recognition in Quiet and in Noise

PubMed Central

Shannon, Robert V.; Cruz, Rachel J.; Galvin, John J.

2011-01-01

High stimulation rates in cochlear implants (CI) offer better temporal sampling, can induce stochastic-like firing of auditory neurons and can increase the electric dynamic range, all of which could improve CI speech performance. While commercial CI have employed increasingly high stimulation rates, no clear or consistent advantage has been shown for high rates. In this study, speech recognition was acutely measured with experimental processors in 7 CI subjects (Clarion CII users). The stimulation rate varied between (approx.) 600 and 4800 pulses per second per electrode (ppse) and the number of active electrodes varied between 4 and 16. Vowel, consonant, consonant-nucleus-consonant word and IEEE sentence recognition was acutely measured in quiet and in steady noise (+10 dB signal-to-noise ratio). Subjective quality ratings were obtained for each of the experimental processors in quiet and in noise. Except for a small difference for vowel recognition in quiet, there were no significant differences in performance among the experimental stimulation rates for any of the speech measures. There was also a small but significant increase in subjective quality rating as stimulation rates increased from 1200 to 2400 ppse in noise. Consistent with previous studies, performance significantly improved as the number of electrodes was increased from 4 to 8, but no significant difference showed between 8, 12 and 16 electrodes. Altogether, there was little-to-no advantage of high stimulation rates in quiet or in noise, at least for the present speech tests and conditions. PMID:20639631

Comparison of Object Recognition Behavior in Human and Monkey

PubMed Central

Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys. PMID:26338324

Facial recognition using simulated prosthetic pixelized vision.

PubMed

Thompson, Robert W; Barnett, G David; Humayun, Mark S; Dagnelie, Gislin

2003-11-01

To evaluate a model of simulated pixelized prosthetic vision using noncontiguous circular phosphenes, to test the effects of phosphene and grid parameters on facial recognition. A video headset was used to view a reference set of four faces, followed by a partially averted image of one of those faces viewed through a square pixelizing grid that contained 10x10 to 32x32 dots separated by gaps. The grid size, dot size, gap width, dot dropout rate, and gray-scale resolution were varied separately about a standard test condition, for a total of 16 conditions. All tests were first performed at 99% contrast and then repeated at 12.5% contrast. Discrimination speed and performance were influenced by all stimulus parameters. The subjects achieved highly significant facial recognition accuracy for all high-contrast tests except for grids with 70% random dot dropout and two gray levels. In low-contrast tests, significant facial recognition accuracy was achieved for all but the most adverse grid parameters: total grid area less than 17% of the target image, 70% dropout, four or fewer gray levels, and a gap of 40.5 arcmin. For difficult test conditions, a pronounced learning effect was noticed during high-contrast trials, and a more subtle practice effect on timing was evident during subsequent low-contrast trials. These findings suggest that reliable face recognition with crude pixelized grids can be learned and may be possible, even with a crude visual prosthesis.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

GNSS seismometer: Seismic phase recognition of real-time high-rate GNSS deformation waves

NASA Astrophysics Data System (ADS)

Nie, Zhaosheng; Zhang, Rui; Liu, Gang; Jia, Zhige; Wang, Dijin; Zhou, Yu; Lin, Mu

2016-12-01

High-rate global navigation satellite systems (GNSS) can potentially be used as seismometers to capture short-period instantaneous dynamic deformation waves from earthquakes. However, the performance and seismic phase recognition of the GNSS seismometer in the real-time mode, which plays an important role in GNSS seismology, are still uncertain. By comparing the results of accuracy and precision of the real-time solution using a shake table test, we found real-time solutions to be consistent with post-processing solutions and independent of sampling rate. In addition, we analyzed the time series of real-time solutions for shake table tests and recent large earthquakes. The results demonstrated that high-rate GNSS have the ability to retrieve most types of seismic waves, including P-, S-, Love, and Rayleigh waves. The main factor limiting its performance in recording seismic phases is the widely used 1-Hz sampling rate. The noise floor also makes recognition of some weak seismic phases difficult. We concluded that the propagation velocities and path of seismic waves, macro characteristics of the high-rate GNSS array, spatial traces of seismic phases, and incorporation of seismographs are all useful in helping to retrieve seismic phases from the high-rate GNSS time series.

Bioinspired Pollen-Like Hierarchical Surface for Efficient Recognition of Target Cancer Cells.

PubMed

Wang, Wenshuo; Yang, Gao; Cui, Haijun; Meng, Jingxin; Wang, Shutao; Jiang, Lei

2017-08-01

The efficient recognition and isolation of rare cancer cells holds great promise for cancer diagnosis and prognosis. In nature, pollens exploit spiky structures to realize recognition and adhesion to stigma. Herein, a bioinspired pollen-like hierarchical surface is developed by replicating the assembly of pollen grains, and efficient and specific recognition to target cancer cells is achieved. The pollen-like surface is fabricated by combining filtering-assisted assembly and soft lithography-based replication of pollen grains of wild chrysanthemum. After modification with a capture agent specific to cancer cells, the pollen-like surface enables the capture of target cancer cells with high efficiency and specificity. In addition, the pollen-like surface not only assures high viability of captured cells but also performs well in cell mixture system and at low cell density. This study represents a good example of constructing cell recognition biointerfaces inspired by pollen-stigma adhesion. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

24 CFR 985.103 - SEMAP score and overall performance rating.

Code of Federal Regulations, 2011 CFR

2011-04-01

... high performer may receive national recognition by the Department and may be given competitive advantage under notices of fund availability. (b) Standard rating. PHAs with SEMAP scores of 60 to 89...

Two processes support visual recognition memory in rhesus monkeys.

PubMed

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Two processes support visual recognition memory in rhesus monkeys

PubMed Central

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-01-01

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans. PMID:22084079

Missing the Boat--Impact of Just Missing Identification as a High-Performing School

ERIC Educational Resources Information Center

Weiner, Jennie; Donaldson, Morgaen; Dougherty, Shaun M.

2017-01-01

This study capitalizes on the performance identification system under the No Child Left Behind waivers to estimate the school-level impact of just missing formal state recognition as a high-performing school. Using a fuzzy regression-discontinuity design and data from the early years of waiver implementation in Rhode Island, we find that, when…

Effect of acute exposure to a complex fragrance on lexical decision performance.

PubMed

Gaygen, Daniel E; Hedge, Alan

2009-01-01

This study tested the effect of acute exposure to a commercial air freshener, derived from fragrant botanical extracts, at an average concentration of 3.16 mg/m(3) total volatile organic compounds on the lexical decision performance of 28 naive participants. Participants attended two 18-min sessions on separate days and were continuously exposed to the fragrance in either the first (F/NF) or second (NF/F) session. Participants were not instructed about the fragrance. Exposure to the fragrance did not affect high-frequency word recognition. However, there was an order of administration effect for low-frequency word recognition accuracy. When the fragrance was administered first before the no-odor control condition, it did not affect accuracy, but when it was administered second after the control condition, it significantly decreased low-frequency word recognition accuracy. Reaction times to low-frequency words were significantly slower than those for high-frequency words, but no effect of either fragrance or order of administration on reaction times was found. The presence of fragrance in the second session apparently served as a distraction that impaired lexical task performance accuracy. The introduction of fragrances into buildings may not necessarily facilitate all aspects of work performance as anticipated.

Magical ideation associated social cognition in adolescents: signs of a negative facial affect recognition deficit.

PubMed

Canli, Derya; Ozdemir, Hatice; Kocak, Orhan Murat

2015-08-01

Studies provide evidence for impaired social cognition in schizotypy and its association with negative symptoms. Cognitive features related to magical ideation - a component of the positive dimension of schizotypy - have been less investigated. We aimed to assess social cognitive functioning among adolescents with high magical ideation scores, mainly focusing on face and emotion recognition. 22 subjects with magical ideation scale scores above the cut off level and 22 controls with lowest scores from among 250 students screened with this scale were included in the study. A face and emotion recognition n-back test, the empathy quotient, theory of mind tests and the Physical Anhedonia Scale were applied to both magical ideation and control groups. The magical ideation group performed significantly worse than controls on both face and emotion recognition tests. Emotion recognition performance was found to be affected by memory load, with sadness, among emotions, revealing a difference between the two groups. Empathy and theory of mind tests did not distinguish the magical ideation group from controls. Our findings provide evidence for a deficit in negative emotion recognition affected by memory load associated with magical ideation in adolescents. Copyright © 2015 Elsevier Inc. All rights reserved.

Non-native Listeners’ Recognition of High-Variability Speech Using PRESTO

PubMed Central

Tamati, Terrin N.; Pisoni, David B.

2015-01-01

Background Natural variability in speech is a significant challenge to robust successful spoken word recognition. In everyday listening environments, listeners must quickly adapt and adjust to multiple sources of variability in both the signal and listening environments. High-variability speech may be particularly difficult to understand for non-native listeners, who have less experience with the second language (L2) phonological system and less detailed knowledge of sociolinguistic variation of the L2. Purpose The purpose of this study was to investigate the effects of high-variability sentences on non-native speech recognition and to explore the underlying sources of individual differences in speech recognition abilities of non-native listeners. Research Design Participants completed two sentence recognition tasks involving high-variability and low-variability sentences. They also completed a battery of behavioral tasks and self-report questionnaires designed to assess their indexical processing skills, vocabulary knowledge, and several core neurocognitive abilities. Study Sample Native speakers of Mandarin (n = 25) living in the United States recruited from the Indiana University community participated in the current study. A native comparison group consisted of scores obtained from native speakers of English (n = 21) in the Indiana University community taken from an earlier study. Data Collection and Analysis Speech recognition in high-variability listening conditions was assessed with a sentence recognition task using sentences from PRESTO (Perceptually Robust English Sentence Test Open-Set) mixed in 6-talker multitalker babble. Speech recognition in low-variability listening conditions was assessed using sentences from HINT (Hearing In Noise Test) mixed in 6-talker multitalker babble. Indexical processing skills were measured using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Vocabulary knowledge was assessed with the WordFam word familiarity test, and executive functioning was assessed with the BRIEF-A (Behavioral Rating Inventory of Executive Function – Adult Version) self-report questionnaire. Scores from the non-native listeners on behavioral tasks and self-report questionnaires were compared with scores obtained from native listeners tested in a previous study and were examined for individual differences. Results Non-native keyword recognition scores were significantly lower on PRESTO sentences than on HINT sentences. Non-native listeners’ keyword recognition scores were also lower than native listeners’ scores on both sentence recognition tasks. Differences in performance on the sentence recognition tasks between non-native and native listeners were larger on PRESTO than on HINT, although group differences varied by signal-to-noise ratio. The non-native and native groups also differed in the ability to categorize talkers by region of origin and in vocabulary knowledge. Individual non-native word recognition accuracy on PRESTO sentences in multitalker babble at more favorable signal-to-noise ratios was found to be related to several BRIEF-A subscales and composite scores. However, non-native performance on PRESTO was not related to regional dialect categorization, talker and gender discrimination, or vocabulary knowledge. Conclusions High-variability sentences in multitalker babble were particularly challenging for non-native listeners. Difficulty under high-variability testing conditions was related to lack of experience with the L2, especially L2 sociolinguistic information, compared with native listeners. Individual differences among the non-native listeners were related to weaknesses in core neurocognitive abilities affecting behavioral control in everyday life. PMID:25405842

Gesture recognition by instantaneous surface EMG images.

PubMed

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-11-15

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses.

Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods

NASA Astrophysics Data System (ADS)

Dinges, David F.; Venkataraman, Sundara; McGlinchey, Eleanor L.; Metaxas, Dimitris N.

2007-02-01

Astronauts are required to perform mission-critical tasks at a high level of functional capability throughout spaceflight. Stressors can compromise their ability to do so, making early objective detection of neurobehavioral problems in spaceflight a priority. Computer optical approaches offer a completely unobtrusive way to detect distress during critical operations in space flight. A methodology was developed and a study completed to determine whether optical computer recognition algorithms could be used to discriminate facial expressions during stress induced by performance demands. Stress recognition from a facial image sequence is a subject that has not received much attention although it is an important problem for many applications beyond space flight (security, human-computer interaction, etc.). This paper proposes a comprehensive method to detect stress from facial image sequences by using a model-based tracker. The image sequences were captured as subjects underwent a battery of psychological tests under high- and low-stress conditions. A cue integration-based tracking system accurately captured the rigid and non-rigid parameters of different parts of the face (eyebrows, lips). The labeled sequences were used to train the recognition system, which consisted of generative (hidden Markov model) and discriminative (support vector machine) parts that yield results superior to using either approach individually. The current optical algorithm methods performed at a 68% accuracy rate in an experimental study of 60 healthy adults undergoing periods of high-stress versus low-stress performance demands. Accuracy and practical feasibility of the technique is being improved further with automatic multi-resolution selection for the discretization of the mask, and automated face detection and mask initialization algorithms.

Controlled synthesis and chiral recognition of immobilized cellulose and amylose tris(cyclohexylcarbamate)s/3-(triethoxysilyl)propylcarbamates as chiral packing materials for high-performance liquid chromatography.

PubMed

Shen, Jun; Liu, Shuangyan; Li, Pengfei; Shen, Xiande; Okamoto, Yoshio

2012-07-13

The cyclohexylcarbamates of cellulose and amylose bearing a controlled amount of 3-(triethoxysilyl)propyl residue were synthesized by a one-pot process and efficiently immobilized onto silica gel through the intermolecular polycondensation of triethoxysilyl group. Their chiral recognition abilities were evaluated as chiral packing materials (CPMs) for high-performance liquid chromatography (HPLC). The immobilized CPMs exhibited comparable or higher recognition abilities than the conventional coated-type CPMs. The universal solvent compatibility of the immobilized CPMs clearly contributes to the improvement of chiral recognition for most racemates used in the present study. Interestingly, a significantly improved resolution for racemic trans-stilbene oxide (α=2.23) could be attained on the immobilized CPM using the eluent containing 30 vol.% chloroform in hexane, which cannot be used for the conventional coated-type CPMs. On the CPMs, almost no resolution of trans-stilbene oxide was attained by a typical eluent, hexane-2-propanol mixture (90/10, v/v). The novel immobilized CPM can also be used in thin-layer chromatography (TLC) due to the absence of an aromatic group. Copyright © 2012 Elsevier B.V. All rights reserved.

Varying face occlusion detection and iterative recovery for face recognition

NASA Astrophysics Data System (ADS)

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

2017-05-01

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Model-based recognition of 3D articulated target using ladar range data.

PubMed

Lv, Dan; Sun, Jian-Feng; Li, Qi; Wang, Qi

2015-06-10

Ladar is suitable for 3D target recognition because ladar range images can provide rich 3D geometric surface information of targets. In this paper, we propose a part-based 3D model matching technique to recognize articulated ground military vehicles in ladar range images. The key of this approach is to solve the decomposition and pose estimation of articulated parts of targets. The articulated components were decomposed into isolate parts based on 3D geometric properties of targets, such as surface point normals, data histogram distribution, and data distance relationships. The corresponding poses of these separate parts were estimated through the linear characteristics of barrels. According to these pose parameters, all parts of the target were roughly aligned to 3D point cloud models in a library and fine matching was finally performed to accomplish 3D articulated target recognition. The recognition performance was evaluated with 1728 ladar range images of eight different articulated military vehicles with various part types and orientations. Experimental results demonstrated that the proposed approach achieved a high recognition rate.

The NTID speech recognition test: NSRT(®).

PubMed

Bochner, Joseph H; Garrison, Wayne M; Doherty, Karen A

2015-07-01

The purpose of this study was to collect and analyse data necessary for expansion of the NSRT item pool and to evaluate the NSRT adaptive testing software. Participants were administered pure-tone and speech recognition tests including W-22 and QuickSIN, as well as a set of 323 new NSRT items and NSRT adaptive tests in quiet and background noise. Performance on the adaptive tests was compared to pure-tone thresholds and performance on other speech recognition measures. The 323 new items were subjected to Rasch scaling analysis. Seventy adults with mild to moderately severe hearing loss participated in this study. Their mean age was 62.4 years (sd = 20.8). The 323 new NSRT items fit very well with the original item bank, enabling the item pool to be more than doubled in size. Data indicate high reliability coefficients for the NSRT and moderate correlations with pure-tone thresholds (PTA and HFPTA) and other speech recognition measures (W-22, QuickSIN, and SRT). The adaptive NSRT is an efficient and effective measure of speech recognition, providing valid and reliable information concerning respondents' speech perception abilities.

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deptuch, Gregory; Hoff, James; Jindariani, Sergo

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The firstmore » step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.« less

Local visual perception bias in children with high-functioning autism spectrum disorders; do we have the whole picture?

PubMed

Falkmer, Marita; Black, Melissa; Tang, Julia; Fitzgerald, Patrick; Girdler, Sonya; Leung, Denise; Ordqvist, Anna; Tan, Tele; Jahan, Ishrat; Falkmer, Torbjorn

2016-01-01

While local bias in visual processing in children with autism spectrum disorders (ASD) has been reported to result in difficulties in recognizing faces and facially expressed emotions, but superior ability in disembedding figures, associations between these abilities within a group of children with and without ASD have not been explored. Possible associations in performance on the Visual Perception Skills Figure-Ground test, a face recognition test and an emotion recognition test were investigated within 25 8-12-years-old children with high-functioning autism/Asperger syndrome, and in comparison to 33 typically developing children. Analyses indicated a weak positive correlation between accuracy in Figure-Ground recognition and emotion recognition. No other correlation estimates were significant. These findings challenge both the enhanced perceptual function hypothesis and the weak central coherence hypothesis, and accentuate the importance of further scrutinizing the existance and nature of local visual bias in ASD.

Cost-sensitive learning for emotion robust speaker recognition.

PubMed

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

PubMed Central

Li, Dongdong; Yang, Yingchun

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. PMID:24999492

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Faces are special but not too special: Spared face recognition in amnesia is based on familiarity

PubMed Central

Aly, Mariam; Knight, Robert T.; Yonelinas, Andrew P.

2014-01-01

Most current theories of human memory are material-general in the sense that they assume that the medial temporal lobe (MTL) is important for retrieving the details of prior events, regardless of the specific type of materials. Recent studies of amnesia have challenged the material-general assumption by suggesting that the MTL may be necessary for remembering words, but is not involved in remembering faces. We examined recognition memory for faces and words in a group of amnesic patients, which included hypoxic patients and patients with extensive left or right MTL lesions. Recognition confidence judgments were used to plot receiver operating characteristics (ROCs) in order to more fully quantify recognition performance and to estimate the contributions of recollection and familiarity. Consistent with the extant literature, an analysis of overall recognition accuracy showed that the patients were impaired at word memory but had spared face memory. However, the ROC analysis indicated that the patients were generally impaired at high confidence recognition responses for faces and words, and they exhibited significant recollection impairments for both types of materials. Familiarity for faces was preserved in all patients, but extensive left MTL damage impaired familiarity for words. These results suggest that face recognition may appear to be spared because performance tends to rely heavily on familiarity, a process that is relatively well preserved in amnesia. The findings challenge material-general theories of memory, and suggest that both material and process are important determinants of memory performance in amnesia, and different types of materials may depend more or less on recollection and familiarity. PMID:20833190

Speech emotion recognition methods: A literature review

NASA Astrophysics Data System (ADS)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

"It's Always the Judge's Fault": Attention, Emotion Recognition, and Expertise in Rhythmic Gymnastics Assessment.

PubMed

van Bokhorst, Lindsey G; Knapová, Lenka; Majoranc, Kim; Szebeni, Zea K; Táborský, Adam; Tomić, Dragana; Cañadas, Elena

2016-01-01

In many sports, such as figure skating or gymnastics, the outcome of a performance does not rely exclusively on objective measurements, but on more subjective cues. Judges need high attentional capacities to process visual information and overcome fatigue. Also their emotion recognition abilities might have an effect in detecting errors and making a more accurate assessment. Moreover, the scoring given by judges could be also influenced by their level of expertise. This study aims to assess how rhythmic gymnastics judges' emotion recognition and attentional abilities influence accuracy of performance assessment. Data will be collected from rhythmic gymnastics judges and coaches at different international levels. This study will employ an online questionnaire consisting on an emotion recognition test and attentional test. Participants' task is to watch a set of videotaped rhythmic gymnastics performances and evaluate them on the artistic and execution components of performance. Their scoring will be compared with the official scores given at the competition the video was taken from to measure the accuracy of the participants' evaluations. The proposed research represents an interdisciplinary approach that integrates cognitive and sport psychology within experimental and applied contexts. The current study advances the theoretical understanding of how emotional and attentional aspects affect the evaluation of sport performance. The results will provide valuable evidence on the direction and strength of the relationship between the above-mentioned factors and the accuracy of sport performance evaluation. Importantly, practical implications might be drawn from this study. Intervention programs directed at improving the accuracy of judges could be created based on the understanding of how emotion recognition and attentional abilities are related to the accuracy of performance assessment.

“It’s Always the Judge’s Fault”: Attention, Emotion Recognition, and Expertise in Rhythmic Gymnastics Assessment

PubMed Central

van Bokhorst, Lindsey G.; Knapová, Lenka; Majoranc, Kim; Szebeni, Zea K.; Táborský, Adam; Tomić, Dragana; Cañadas, Elena

2016-01-01

In many sports, such as figure skating or gymnastics, the outcome of a performance does not rely exclusively on objective measurements, but on more subjective cues. Judges need high attentional capacities to process visual information and overcome fatigue. Also their emotion recognition abilities might have an effect in detecting errors and making a more accurate assessment. Moreover, the scoring given by judges could be also influenced by their level of expertise. This study aims to assess how rhythmic gymnastics judges’ emotion recognition and attentional abilities influence accuracy of performance assessment. Data will be collected from rhythmic gymnastics judges and coaches at different international levels. This study will employ an online questionnaire consisting on an emotion recognition test and attentional test. Participants’ task is to watch a set of videotaped rhythmic gymnastics performances and evaluate them on the artistic and execution components of performance. Their scoring will be compared with the official scores given at the competition the video was taken from to measure the accuracy of the participants’ evaluations. The proposed research represents an interdisciplinary approach that integrates cognitive and sport psychology within experimental and applied contexts. The current study advances the theoretical understanding of how emotional and attentional aspects affect the evaluation of sport performance. The results will provide valuable evidence on the direction and strength of the relationship between the above-mentioned factors and the accuracy of sport performance evaluation. Importantly, practical implications might be drawn from this study. Intervention programs directed at improving the accuracy of judges could be created based on the understanding of how emotion recognition and attentional abilities are related to the accuracy of performance assessment. PMID:27458406

Impaired Word and Face Recognition in Older Adults with Type 2 Diabetes.

PubMed

Jones, Nicola; Riby, Leigh M; Smith, Michael A

2016-07-01

Older adults with type 2 diabetes mellitus (DM2) exhibit accelerated decline in some domains of cognition including verbal episodic memory. Few studies have investigated the influence of DM2 status in older adults on recognition memory for more complex stimuli such as faces. In the present study we sought to compare recognition memory performance for words, objects and faces under conditions of relatively low and high cognitive load. Healthy older adults with good glucoregulatory control (n = 13) and older adults with DM2 (n = 24) were administered recognition memory tasks in which stimuli (faces, objects and words) were presented under conditions of either i) low (stimulus presented without a background pattern) or ii) high (stimulus presented against a background pattern) cognitive load. In a subsequent recognition phase, the DM2 group recognized fewer faces than healthy controls. Further, the DM2 group exhibited word recognition deficits in the low cognitive load condition. The recognition memory impairment observed in patients with DM2 has clear implications for day-to-day functioning. Although these deficits were not amplified under conditions of increased cognitive load, the present study emphasizes that recognition memory impairment for both words and more complex stimuli such as face are a feature of DM2 in older adults. Copyright © 2016 IMSS. Published by Elsevier Inc. All rights reserved.

Conduct symptoms and emotion recognition in adolescent boys with externalization problems.

PubMed

Aspan, Nikoletta; Vida, Peter; Gadoros, Julia; Halasz, Jozsef

2013-01-01

In adults with antisocial personality disorder, marked alterations in the recognition of facial affect were described. Less consistent data are available on the emotion recognition in adolescents with externalization problems. The aim of the present study was to assess the relation between the recognition of emotions and conduct symptoms in adolescent boys with externalization problems. Adolescent boys with externalization problems referred to Vadaskert Child Psychiatry Hospital participated in the study after informed consent (N = 114, 11-17 years, mean = 13.4). The conduct problems scale of the strengths and difficulties questionnaire (parent and self-report) was used. The performance in a facial emotion recognition test was assessed. Conduct problems score (parent and self-report) was inversely correlated with the overall emotion recognition. In the self-report, conduct problems score was inversely correlated with the recognition of anger, fear, and sadness. Adolescents with high conduct problems scores were significantly worse in the recognition of fear, sadness, and overall recognition than adolescents with low conduct scores, irrespective of age and IQ. Our results suggest that impaired emotion recognition is dimensionally related to conduct problems and might have importance in the development of antisocial behavior.

Creating Organizational Structures to Facilitate Collegial Interaction among Teachers: Evidence from a High-Performing Urban Midwestern US District

ERIC Educational Resources Information Center

Ford, Timothy G.; Youngs, Peter A.

2018-01-01

Emerging from concerns about "contrived collegiality" in schools is also the recognition that breaking existing patterns of collegial interaction (or lack thereof) might necessitate some level of leader-initiated (or otherwise organizational) intervention. This paper presents the case of Middleville, a high-performing Midwestern US…

DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2015-09-30

Clark (2014), "Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia...34Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia Computer Science 20

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

Real-Time Pattern Recognition - An Industrial Example

NASA Astrophysics Data System (ADS)

Fitton, Gary M.

1981-11-01

Rapid advancements in cost effective sensors and micro computers are now making practical the on-line implementation of pattern recognition based systems for a variety of industrial applications requiring high processing speeds. One major application area for real time pattern recognition is in the sorting of packaged/cartoned goods at high speed for automated warehousing and return goods cataloging. While there are many OCR and bar code readers available to perform these functions, it is often impractical to use such codes (package too small, adverse esthetics, poor print quality) and an approach which recognizes an item by its graphic content alone is desirable. This paper describes a specific application within the tobacco industry, that of sorting returned cigarette goods by brand and size.

Monopolar Detection Thresholds Predict Spatial Selectivity of Neural Excitation in Cochlear Implants: Implications for Speech Recognition

PubMed Central

2016-01-01

The objectives of the study were to (1) investigate the potential of using monopolar psychophysical detection thresholds for estimating spatial selectivity of neural excitation with cochlear implants and to (2) examine the effect of site removal on speech recognition based on the threshold measure. Detection thresholds were measured in Cochlear Nucleus® device users using monopolar stimulation for pulse trains that were of (a) low rate and long duration, (b) high rate and short duration, and (c) high rate and long duration. Spatial selectivity of neural excitation was estimated by a forward-masking paradigm, where the probe threshold elevation in the presence of a forward masker was measured as a function of masker-probe separation. The strength of the correlation between the monopolar thresholds and the slopes of the masking patterns systematically reduced as neural response of the threshold stimulus involved interpulse interactions (refractoriness and sub-threshold adaptation), and spike-rate adaptation. Detection threshold for the low-rate stimulus most strongly correlated with the spread of forward masking patterns and the correlation reduced for long and high rate pulse trains. The low-rate thresholds were then measured for all electrodes across the array for each subject. Subsequently, speech recognition was tested with experimental maps that deactivated five stimulation sites with the highest thresholds and five randomly chosen ones. Performance with deactivating the high-threshold sites was better than performance with the subjects’ clinical map used every day with all electrodes active, in both quiet and background noise. Performance with random deactivation was on average poorer than that with the clinical map but the difference was not significant. These results suggested that the monopolar low-rate thresholds are related to the spatial neural excitation patterns in cochlear implant users and can be used to select sites for more optimal speech recognition performance. PMID:27798658

Mathematical morphology-based shape feature analysis for Chinese character recognition systems

NASA Astrophysics Data System (ADS)

Pai, Tun-Wen; Shyu, Keh-Hwa; Chen, Ling-Fan; Tai, Gwo-Chin

1995-04-01

This paper proposes an efficient technique of shape feature extraction based on the application of mathematical morphology theory. A new shape complexity index for preclassification of machine printed Chinese Character Recognition (CCR) is also proposed. For characters represented in different fonts/sizes or in a low resolution environment, a more stable local feature such as shape structure is preferred for character recognition. Morphological valley extraction filters are applied to extract the protrusive strokes from four sides of an input Chinese character. The number of extracted local strokes reflects the shape complexity of each side. These shape features of characters are encoded as corresponding shape complexity indices. Based on the shape complexity index, data base is able to be classified into 16 groups prior to recognition procedures. The performance of associating with shape feature analysis reclaims several characters from misrecognized character sets and results in an average of 3.3% improvement of recognition rate from an existing recognition system. In addition to enhance the recognition performance, the extracted stroke information can be further analyzed and classified its own stroke type. Therefore, the combination of extracted strokes from each side provides a means for data base clustering based on radical or subword components. It is one of the best solutions for recognizing high complexity characters such as Chinese characters which are divided into more than 200 different categories and consist more than 13,000 characters.

Biometric iris image acquisition system with wavefront coding technology

NASA Astrophysics Data System (ADS)

Hsieh, Sheng-Hsun; Yang, Hsi-Wen; Huang, Shao-Hung; Li, Yung-Hui; Tien, Chung-Hao

2013-09-01

Biometric signatures for identity recognition have been practiced for centuries. Basically, the personal attributes used for a biometric identification system can be classified into two areas: one is based on physiological attributes, such as DNA, facial features, retinal vasculature, fingerprint, hand geometry, iris texture and so on; the other scenario is dependent on the individual behavioral attributes, such as signature, keystroke, voice and gait style. Among these features, iris recognition is one of the most attractive approaches due to its nature of randomness, texture stability over a life time, high entropy density and non-invasive acquisition. While the performance of iris recognition on high quality image is well investigated, not too many studies addressed that how iris recognition performs subject to non-ideal image data, especially when the data is acquired in challenging conditions, such as long working distance, dynamical movement of subjects, uncontrolled illumination conditions and so on. There are three main contributions in this paper. Firstly, the optical system parameters, such as magnification and field of view, was optimally designed through the first-order optics. Secondly, the irradiance constraints was derived by optical conservation theorem. Through the relationship between the subject and the detector, we could estimate the limitation of working distance when the camera lens and CCD sensor were known. The working distance is set to 3m in our system with pupil diameter 86mm and CCD irradiance 0.3mW/cm2. Finally, We employed a hybrid scheme combining eye tracking with pan and tilt system, wavefront coding technology, filter optimization and post signal recognition to implement a robust iris recognition system in dynamic operation. The blurred image was restored to ensure recognition accuracy over 3m working distance with 400mm focal length and aperture F/6.3 optics. The simulation result as well as experiment validates the proposed code apertured imaging system, where the imaging volume was 2.57 times extended over the traditional optics, while keeping sufficient recognition accuracy.

Image dependency in the recognition of newly learnt faces.

PubMed

Longmore, Christopher A; Santos, Isabel M; Silva, Carlos F; Hall, Abi; Faloyin, Dipo; Little, Emily

2017-05-01

Research investigating the effect of lighting and viewpoint changes on unfamiliar and newly learnt faces has revealed that such recognition is highly image dependent and that changes in either of these leads to poor recognition accuracy. Three experiments are reported to extend these findings by examining the effect of apparent age on the recognition of newly learnt faces. Experiment 1 investigated the ability to generalize to novel ages of a face after learning a single image. It was found that recognition was best for the learnt image with performance falling the greater the dissimilarity between the study and test images. Experiments 2 and 3 examined whether learning two images aids subsequent recognition of a novel image. The results indicated that interpolation between two studied images (Experiment 2) provided some additional benefit over learning a single view, but that this did not extend to extrapolation (Experiment 3). The results from all studies suggest that recognition was driven primarily by pictorial codes and that the recognition of faces learnt from a limited number of sources operates on stored images of faces as opposed to more abstract, structural, representations.

Speaker recognition with temporal cues in acoustic and electric hearing

NASA Astrophysics Data System (ADS)

Vongphoe, Michael; Zeng, Fan-Gang

2005-08-01

Natural spoken language processing includes not only speech recognition but also identification of the speaker's gender, age, emotional, and social status. Our purpose in this study is to evaluate whether temporal cues are sufficient to support both speech and speaker recognition. Ten cochlear-implant and six normal-hearing subjects were presented with vowel tokens spoken by three men, three women, two boys, and two girls. In one condition, the subject was asked to recognize the vowel. In the other condition, the subject was asked to identify the speaker. Extensive training was provided for the speaker recognition task. Normal-hearing subjects achieved nearly perfect performance in both tasks. Cochlear-implant subjects achieved good performance in vowel recognition but poor performance in speaker recognition. The level of the cochlear implant performance was functionally equivalent to normal performance with eight spectral bands for vowel recognition but only to one band for speaker recognition. These results show a disassociation between speech and speaker recognition with primarily temporal cues, highlighting the limitation of current speech processing strategies in cochlear implants. Several methods, including explicit encoding of fundamental frequency and frequency modulation, are proposed to improve speaker recognition for current cochlear implant users.

Identifying and detecting facial expressions of emotion in peripheral vision.

PubMed

Smith, Fraser W; Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus.

Identifying and detecting facial expressions of emotion in peripheral vision

PubMed Central

Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus. PMID:29847562

A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views.

PubMed

Chaaraoui, Alexandros Andre; Flórez-Revuelta, Francisco

2014-01-01

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Gesture recognition by instantaneous surface EMG images

PubMed Central

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-01-01

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses. PMID:27845347

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles †

PubMed Central

López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

2015-01-01

Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

Dual-process models of associative recognition in young and older adults: evidence from receiver operating characteristics.

PubMed

Healy, Michael R; Light, Leah L; Chung, Christie

2005-07-01

In 3 experiments, young and older adults studied lists of unrelated word pairs and were given confidence-rated item and associative recognition tests. Several different models of recognition were fit to the confidence-rating data using techniques described by S. Macho (2002, 2004). Concordant with previous findings, item recognition data were best fit by an unequal-variance signal detection theory model for both young and older adults. For both age groups, associative recognition performance was best explained by models incorporating both recollection and familiarity components. Examination of parameter estimates supported the conclusion that recollection is reduced in old age, but inferences about age differences in familiarity were highly model dependent. Implications for dual-process models of memory in old age are discussed. ((c) 2005 APA, all rights reserved).

Conic section function neural network circuitry for offline signature recognition.

PubMed

Erkmen, Burcu; Kahraman, Nihan; Vural, Revna A; Yildirim, Tulay

2010-04-01

In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

Auditory-motor learning influences auditory memory for music.

PubMed

Brown, Rachel M; Palmer, Caroline

2012-05-01

In two experiments, we investigated how auditory-motor learning influences performers' memory for music. Skilled pianists learned novel melodies in four conditions: auditory only (listening), motor only (performing without sound), strongly coupled auditory-motor (normal performance), and weakly coupled auditory-motor (performing along with auditory recordings). Pianists' recognition of the learned melodies was better following auditory-only or auditory-motor (weakly coupled and strongly coupled) learning than following motor-only learning, and better following strongly coupled auditory-motor learning than following auditory-only learning. Auditory and motor imagery abilities modulated the learning effects: Pianists with high auditory imagery scores had better recognition following motor-only learning, suggesting that auditory imagery compensated for missing auditory feedback at the learning stage. Experiment 2 replicated the findings of Experiment 1 with melodies that contained greater variation in acoustic features. Melodies that were slower and less variable in tempo and intensity were remembered better following weakly coupled auditory-motor learning. These findings suggest that motor learning can aid performers' auditory recognition of music beyond auditory learning alone, and that motor learning is influenced by individual abilities in mental imagery and by variation in acoustic features.

Verbal Serial List Learning in Mild Cognitive Impairment: A Profile Analysis of Interference, Forgetting, and Errors

PubMed Central

Libon, David J.; Bondi, Mark W.; Price, Catherine C.; Lamar, Melissa; Eppig, Joel; Wambach, Denene M.; Nieves, Christine; Delano-Wood, Lisa; Giovannetti, Tania; Lippa, Carol; Kabasakalian, Anahid; Cosentino, Stephanie; Swenson, Rod; Penney, Dana L.

2012-01-01

Using cluster analysis Libon et al. (2010) found three verbal serial list-learning profiles involving delay memory test performance in patients with mild cognitive impairment (MCI). Amnesic MCI (aMCI) patients presented with low scores on delay free recall and recognition tests; mixed MCI (mxMCI) patients scored higher on recognition compared to delay free recall tests; and dysexecutive MCI (dMCI) patients generated relatively intact scores on both delay test conditions. The aim of the current research was to further characterize memory impairment in MCI by examining forgetting/savings, interference from a competing word list, intrusion errors/perseverations, intrusion word frequency, and recognition foils in these three statistically determined MCI groups compared to normal control (NC) participants. The aMCI patients exhibited little savings, generated more highly prototypic intrusion errors, and displayed indiscriminate responding to delayed recognition foils. The mxMCI patients exhibited higher saving scores, fewer and less prototypic intrusion errors, and selectively endorsed recognition foils from the interference list. dMCI patients also selectively endorsed recognition foils from the interference list but performed similarly compared to NC participants. These data suggest the existence of distinct memory impairments in MCI and caution against the routine use of a single memory test score to operationally define MCI. PMID:21880171

A Failed Marriage between Standardization and Incentivism: Divergent Perspectives on Performance-Based Compensation in Shanghai

ERIC Educational Resources Information Center

La Londe, Priya G.

2017-01-01

The Chinese province of Shanghai has gained international recognition as a high performing education system with strong teaching and learning outcomes. One accountability mechanism in Shanghai's education reform strategy is statewide performance-based compensation (PBC), also known as performance- or merit pay. Providing a first time account of…

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Human-Computer Interface Controlled by Horizontal Directional Eye Movements and Voluntary Blinks Using AC EOG Signals

NASA Astrophysics Data System (ADS)

Kajiwara, Yusuke; Murata, Hiroaki; Kimura, Haruhiko; Abe, Koji

As a communication support tool for cases of amyotrophic lateral sclerosis (ALS), researches on eye gaze human-computer interfaces have been active. However, since voluntary and involuntary eye movements cannot be distinguished in the interfaces, their performance is still not sufficient for practical use. This paper presents a high performance human-computer interface system which unites high quality recognitions of horizontal directional eye movements and voluntary blinks. The experimental results have shown that the number of incorrect inputs is decreased by 35.1% in an existing system which equips recognitions of horizontal and vertical directional eye movements in addition to voluntary blinks and character inputs are speeded up by 17.4% from the existing system.

Melodic Contour Identification and Music Perception by Cochlear Implant Users

PubMed Central

Galvin, John J.; Fu, Qian-Jie; Shannon, Robert V.

2013-01-01

Research and outcomes with cochlear implants (CIs) have revealed a dichotomy in the cues necessary for speech and music recognition. CI devices typically transmit 16–22 spectral channels, each modulated slowly in time. This coarse representation provides enough information to support speech understanding in quiet and rhythmic perception in music, but not enough to support speech understanding in noise or melody recognition. Melody recognition requires some capacity for complex pitch perception, which in turn depends strongly on access to spectral fine structure cues. Thus, temporal envelope cues are adequate for speech perception under optimal listening conditions, while spectral fine structure cues are needed for music perception. In this paper, we present recent experiments that directly measure CI users’ melodic pitch perception using a melodic contour identification (MCI) task. While normal-hearing (NH) listeners’ performance was consistently high across experiments, MCI performance was highly variable across CI users. CI users’ MCI performance was significantly affected by instrument timbre, as well as by the presence of a competing instrument. In general, CI users had great difficulty extracting melodic pitch from complex stimuli. However, musically-experienced CI users often performed as well as NH listeners, and MCI training in less experienced subjects greatly improved performance. With fixed constraints on spectral resolution, such as it occurs with hearing loss or an auditory prosthesis, training and experience can provide a considerable improvements in music perception and appreciation. PMID:19673835

Finger Vein Recognition Based on a Personalized Best Bit Map

PubMed Central

Yang, Gongping; Xi, Xiaoming; Yin, Yilong

2012-01-01

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition. PMID:22438735

Finger vein recognition based on a personalized best bit map.

PubMed

Yang, Gongping; Xi, Xiaoming; Yin, Yilong

2012-01-01

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.

Practical vision based degraded text recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%) using customized systems generated by our development framework in two industrial OCR applications: water bottle label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and many states of art methods, the proposed system shows excellent results.

Job stress, recognition, job performance and intention to stay at work among Jordanian hospital nurses.

PubMed

AbuAlRub, Raeda Fawzi; Al-Zaru, Ibtisam Moawiah

2008-04-01

To investigate: (1) relationships between job stress, recognition of nurses' performance, job performance and intention to stay among hospital nurses; and (2) the buffering effect of recognition of staff performance on the 'stress-intention to stay at work' relationship. Workplace stress tremendously affects today's workforce. Recognition of nurses' performance needs further investigation to determine if it enhances the level of intention to stay at work and if it can buffer the negative effects of stress on nurses' intention to stay at work. The sample of the present study was a convenience one. It consisted of 206 Jordanian staff nurses who completed a structured questionnaire. The findings of the study indicated a direct and a buffering effect of recognition of nurses' performance on job stress and the level of intention to stay at work. The results of the study indicated the importance of recognition for outstanding performance as well as achievements. Implications for nursing management The results of this study support the need to focus on the implementation of recognition strategies in the workplace to reduce job stress and enhance retention.

The benefits of mystery in nature on attention: assessing the impacts of presentation duration

PubMed Central

Szolosi, Andrew M.; Watson, Jason M.; Ruddell, Edward J.

2014-01-01

Although research has provided prodigious evidence in support of the cognitive benefits that natural settings have over urban settings, all nature is not equal. Within nature, natural settings that contain mystery are often among the most preferred nature scenes. With the prospect of acquiring new information, scenes of this type could more effectively elicit a person's sense of fascination, enabling that person to rest the more effortful forms of attention. The present study examined the direct cognitive benefits that mystery in nature has on attention. Settings of this sort presumably evoke a form of attention that is undemanding or effortless. In order to investigate that notion, participants (n = 144) completed a Recognition Memory Task (RMT) that evaluated recognition performance based on the presence of mystery and presentation duration (300 ms, 1 s, 5 s, and 10 s). Results revealed that with additional viewing time, images perceived high in mystery achieved greater improvements in recognition performance when compared to those images perceived low in mystery. Tests for mediation showed that the effect mystery had on recognition performance occurred through perceptions of fascination. Implications of these and other findings are discussed in the context of Attention Restoration Theory. PMID:25505441

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

Deep learning and face recognition: the state of the art

NASA Astrophysics Data System (ADS)

Balaban, Stephen

2015-05-01

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.

Multimodal biometric method that combines veins, prints, and shape of a finger

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Kim, Jeong Nyeo

2011-01-01

Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.

A unified framework for gesture recognition and spatiotemporal gesture segmentation.

PubMed

Alon, Jonathan; Athitsos, Vassilis; Yuan, Quan; Sclaroff, Stan

2009-09-01

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

NASA Astrophysics Data System (ADS)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Intact anger recognition in depression despite aberrant visual facial information usage.

PubMed

Clark, Cameron M; Chiu, Carina G; Diaz, Ruth L; Goghari, Vina M

2014-08-01

Previous literature has indicated abnormalities in facial emotion recognition abilities, as well as deficits in basic visual processes in major depression. However, the literature is unclear on a number of important factors including whether or not these abnormalities represent deficient or enhanced emotion recognition abilities compared to control populations, and the degree to which basic visual deficits might impact this process. The present study investigated emotion recognition abilities for angry versus neutral facial expressions in a sample of undergraduate students with Beck Depression Inventory-II (BDI-II) scores indicative of moderate depression (i.e., ≥20), compared to matched low-BDI-II score (i.e., ≤2) controls via the Bubbles Facial Emotion Perception Task. Results indicated unimpaired behavioural performance in discriminating angry from neutral expressions in the high depressive symptoms group relative to the minimal depressive symptoms group, despite evidence of an abnormal pattern of visual facial information usage. The generalizability of the current findings is limited by the highly structured nature of the facial emotion recognition task used, as well as the use of an analog sample undergraduates scoring high in self-rated symptoms of depression rather than a clinical sample. Our findings suggest that basic visual processes are involved in emotion recognition abnormalities in depression, demonstrating consistency with the emotion recognition literature in other psychopathologies (e.g., schizophrenia, autism, social anxiety). Future research should seek to replicate these findings in clinical populations with major depression, and assess the association between aberrant face gaze behaviours and symptom severity and social functioning. Copyright © 2014 Elsevier B.V. All rights reserved.

Strategies for distant speech recognitionin reverberant environments

NASA Astrophysics Data System (ADS)

Delcroix, Marc; Yoshioka, Takuya; Ogawa, Atsunori; Kubo, Yotaro; Fujimoto, Masakiyo; Ito, Nobutaka; Kinoshita, Keisuke; Espi, Miquel; Araki, Shoko; Hori, Takaaki; Nakatani, Tomohiro

2015-12-01

Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.

The effect of mild acute stress during memory consolidation on emotional recognition memory.

PubMed

Corbett, Brittany; Weinberg, Lisa; Duarte, Audrey

2017-11-01

Stress during consolidation improves recognition memory performance. Generally, this memory benefit is greater for emotionally arousing stimuli than neutral stimuli. The strength of the stressor also plays a role in memory performance, with memory performance improving up to a moderate level of stress and thereafter worsening. As our daily stressors are generally minimal in strength, we chose to induce mild acute stress to determine its effect on memory performance. In the current study, we investigated if mild acute stress during consolidation improves memory performance for emotionally arousing images. To investigate this, we had participants encode highly arousing negative, minimally arousing negative, and neutral images. We induced stress using the Montreal Imaging Stress Task (MIST) in half of the participants and a control task to the other half of the participants directly after encoding (i.e. during consolidation) and tested recognition 48h later. We found no difference in memory performance between the stress and control group. We found a graded pattern among confidence, with responders in the stress group having the least amount of confidence in their hits and controls having the most. Across groups, we found highly arousing negative images were better remembered than minimally arousing negative or neutral images. Although stress did not affect memory accuracy, responders, as defined by cortisol reactivity, were less confident in their decisions. Our results suggest that the daily stressors humans experience, regardless of their emotional affect, do not have adverse effects on memory. Copyright © 2017 Elsevier Inc. All rights reserved.

Instructions to mimic improve facial emotion recognition in people with sub-clinical autism traits.

PubMed

Lewis, Michael B; Dunn, Emily

2017-11-01

People tend to mimic the facial expression of others. It has been suggested that this helps provide social glue between affiliated people but it could also aid recognition of emotions through embodied cognition. The degree of facial mimicry, however, varies between individuals and is limited in people with autism spectrum conditions (ASC). The present study sought to investigate the effect of promoting facial mimicry during a facial-emotion-recognition test. In two experiments, participants without an ASC diagnosis had their autism quotient (AQ) measured. Following a baseline test, they did an emotion-recognition test again but half of the participants were asked to mimic the target face they saw prior to making their responses. Mimicry improved emotion recognition, and further analysis revealed that the largest improvement was for participants who had higher scores on the autism traits. In fact, recognition performance was best overall for people who had high AQ scores but also received the instruction to mimic. Implications for people with ASC are explored.

Implementation of age and gender recognition system for intelligent digital signage

NASA Astrophysics Data System (ADS)

Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

2015-12-01

Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

NASA Astrophysics Data System (ADS)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

Enhancing Adsorption Capacity while Maintaining Specific Recognition Performance of Mesoporous Silica: A Novel Imprinting Strategy with Amphiphilic Ionic Liquid as Surfactant.

PubMed

Ding, Shichao; Li, Zhiling; Cheng, Yuan; Du, Chunbao; Gao, Junfeng; Zhang, Yong-Wei; Zhang, Nan; Li, Zhaotong; Chang, Ninghui; Hu, Xiaoling

2018-06-21

In order to facilitate the broad applications of molecular recognition materials in biomedical areas, it is critical to enhance their adsorption capacity while maintaining their excellent recognition performance. In this work, we designed and synthesized well-defined peptide-imprinted mesoporous silica (PIMS) for specific recognition of an immunostimulating hexapeptide from human casein (IHHC) by using amphiphilic ionic liquid as the surfactant to anchor IHHC via a combination of one step sol-gel method and docking oriented imprinting approach. Thereinto, theoretical calculation was employed to reveal the multiple binding interactions and dual-template configuration between amphiphilic ionic liquid and IHHC. The fabricated PIMS was characterized and an in-depth analysis of specific recognition mechanism was conducted. Results revealed that both adsorption and recognition capabilities of PIMS far exceeded that of the NIMS's. More significantly, the PIMS exhibited a superior binding capacity (60.5 mg g-1), which could increase 18.9% than the previous work. The corresponding imprinting factor and selectivity coefficient could reach up to 4.51 and 3.30, respectively. The PIMS also possessed lickety-split kinetic binding for IHHC, which the equilibrium time was only 10 min. All of these merits were due to the high surface area and the synergistic effect of multiple interactions (including hydrogen bonding, π-π stacking, ion-ion electrostatic interactions and van der Waals interactions, etc.) between PIMS and IHHC in imprinted sites. The present work suggests the potential application of PIMS for large-scale and high-effective separation of IHHC, which may lead to their broad applications in drug/gene deliver, biosensors, catalyst and so on. © 2018 IOP Publishing Ltd.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

PubMed

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-10-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

PubMed Central

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-01-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods. PMID:28974031

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

A novel binary shape context for 3D local surface description

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Li, Bijun; Zang, Yufu

2017-08-01

3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.

Integrated system for automated financial document processing

NASA Astrophysics Data System (ADS)

Hassanein, Khaled S.; Wesolkowski, Slawo; Higgins, Ray; Crabtree, Ralph; Peng, Antai

1997-02-01

A system was developed that integrates intelligent document analysis with multiple character/numeral recognition engines in order to achieve high accuracy automated financial document processing. In this system, images are accepted in both their grayscale and binary formats. A document analysis module starts by extracting essential features from the document to help identify its type (e.g. personal check, business check, etc.). These features are also utilized to conduct a full analysis of the image to determine the location of interesting zones such as the courtesy amount and the legal amount. These fields are then made available to several recognition knowledge sources such as courtesy amount recognition engines and legal amount recognition engines through a blackboard architecture. This architecture allows all the available knowledge sources to contribute incrementally and opportunistically to the solution of the given recognition query. Performance results on a test set of machine printed business checks using the integrated system are also reported.

64 x 64 thresholding photodetector array for optical pattern recognition

NASA Astrophysics Data System (ADS)

Langenbacher, Harry; Chao, Tien-Hsin; Shaw, Timothy; Yu, Jeffrey W.

1993-10-01

A high performance 32 X 32 peak detector array is introduced. This detector consists of a 32 X 32 array of thresholding photo-transistor cells, manufactured with a standard MOSIS digital 2-micron CMOS process. A built-in thresholding function that is able to perform 1024 thresholding operations in parallel strongly distinguishes this chip from available CCD detectors. This high speed detector offers responses from one to 10 milliseconds that is much higher than the commercially available CCD detectors operating at a TV frame rate. The parallel multiple peaks thresholding detection capability makes it particularly suitable for optical correlator and optoelectronically implemented neural networks. The principle of operation, circuit design and the performance characteristics are described. Experimental demonstration of correlation peak detection is also provided. Recently, we have also designed and built an advanced version of a 64 X 64 thresholding photodetector array chip. Experimental investigation of using this chip for pattern recognition is ongoing.

Comparing an FPGA to a Cell for an Image Processing Application

NASA Astrophysics Data System (ADS)

Rakvic, Ryan N.; Ngo, Hau; Broussard, Randy P.; Ives, Robert W.

2010-12-01

Modern advancements in configurable hardware, most notably Field-Programmable Gate Arrays (FPGAs), have provided an exciting opportunity to discover the parallel nature of modern image processing algorithms. On the other hand, PlayStation3 (PS3) game consoles contain a multicore heterogeneous processor known as the Cell, which is designed to perform complex image processing algorithms at a high performance. In this research project, our aim is to study the differences in performance of a modern image processing algorithm on these two hardware platforms. In particular, Iris Recognition Systems have recently become an attractive identification method because of their extremely high accuracy. Iris matching, a repeatedly executed portion of a modern iris recognition algorithm, is parallelized on an FPGA system and a Cell processor. We demonstrate a 2.5 times speedup of the parallelized algorithm on the FPGA system when compared to a Cell processor-based version.

Face recognition performance of individuals with Asperger syndrome on the Cambridge Face Memory Test.

PubMed

Hedley, Darren; Brewer, Neil; Young, Robyn

2011-12-01

Although face recognition deficits in individuals with Autism Spectrum Disorder (ASD), including Asperger syndrome (AS), are widely acknowledged, the empirical evidence is mixed. This in part reflects the failure to use standardized and psychometrically sound tests. We contrasted standardized face recognition scores on the Cambridge Face Memory Test (CFMT) for 34 individuals with AS with those for 42, IQ-matched non-ASD individuals, and age-standardized scores from a large Australian cohort. We also examined the influence of IQ, autistic traits, and negative affect on face recognition performance. Overall, participants with AS performed significantly worse on the CFMT than the non-ASD participants and when evaluated against standardized test norms. However, while 24% of participants with AS presented with severe face recognition impairment (>2 SDs below the mean), many individuals performed at or above the typical level for their age: 53% scored within +/- 1 SD of the mean and 9% demonstrated superior performance (>1 SD above the mean). Regression analysis provided no evidence that IQ, autistic traits, or negative affect significantly influenced face recognition: diagnostic group membership was the only significant predictor of face recognition performance. In sum, face recognition performance in ASD is on a continuum, but with average levels significantly below non-ASD levels of performance. Copyright © 2011, International Society for Autism Research, Wiley-Liss, Inc.

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

PubMed

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings.

PubMed

Oerlemans, Anoek M; van der Meer, Jolanda M J; van Steijn, Daphne J; de Ruiter, Saskia W; de Bruijn, Yvette G E; de Sonneville, Leo M J; Buitelaar, Jan K; Rommelse, Nanda N J

2014-05-01

Autism is a highly heritable and clinically heterogeneous neuropsychiatric disorder that frequently co-occurs with other psychopathologies, such as attention-deficit/hyperactivity disorder (ADHD). An approach to parse heterogeneity is by forming more homogeneous subgroups of autism spectrum disorder (ASD) patients based on their underlying, heritable cognitive vulnerabilities (endophenotypes). Emotion recognition is a likely endophenotypic candidate for ASD and possibly for ADHD. Therefore, this study aimed to examine whether emotion recognition is a viable endophenotypic candidate for ASD and to assess the impact of comorbid ADHD in this context. A total of 90 children with ASD (43 with and 47 without ADHD), 79 ASD unaffected siblings, and 139 controls aged 6-13 years, were included to test recognition of facial emotion and affective prosody. Our results revealed that the recognition of both facial emotion and affective prosody was impaired in children with ASD and aggravated by the presence of ADHD. The latter could only be partly explained by typical ADHD cognitive deficits, such as inhibitory and attentional problems. The performance of unaffected siblings could overall be considered at an intermediate level, performing somewhat worse than the controls and better than the ASD probands. Our findings suggest that emotion recognition might be a viable endophenotype in ASD and a fruitful target in future family studies of the genetic contribution to ASD and comorbid ADHD. Furthermore, our results suggest that children with comorbid ASD and ADHD are at highest risk for emotion recognition problems.

Verbal recall and recognition in twins discordant for schizophrenia

PubMed Central

van Erp, Theo G.M.; Therman, Sebastian; Pirkola, Tiia; Tuulio-Henriksson, Annamari; Glahn, David C.; Bachman, Peter; Huttunen, Matti O.; Lönnqvist, Jouko; Hietanen, Marja; Kaprio, Jaakko; Koskenvuo, Markku; Cannon, Tyrone D.

2008-01-01

The nature, neural underpinnings, and etiology of deficits in verbal declarative memory in patients with schizophrenia remain unclear. To examine the contributions of genes and environment to verbal recall and recognition performance in this disorder, the California Verbal Learning Test was administered to a large population-based Finnish twin sample, which included schizophrenic and schizoaffective patients, their non-ill monozygotic (MZ) and dizygotic (DZ) co-twins, and healthy control twins. Compared with controls, patients and their co-twins showed relatively greater performance deficits on free recall compared with recognition. Intra-pair differences between patients and their non-ill co-twins in hippocampal volume and memory performance were highly positively correlated. These findings are consistent with the view that genetic influences are associated with reduced verbal recall in schizophrenia, but that non-genetic influences further compromise these abnormalities in patients who manifest the full-blown schizophrenia phenotype, with this additional degree of disease-related declarative memory deficit mediated in part by hippocampal pathology. PMID:18442861

Emotion recognition in Parkinson's disease: Static and dynamic factors.

PubMed

Wasser, Cory I; Evans, Felicity; Kempnich, Clare; Glikmann-Johnston, Yifat; Andrews, Sophie C; Thyagarajan, Dominic; Stout, Julie C

2018-02-01

The authors tested the hypothesis that Parkinson's disease (PD) participants would perform better in an emotion recognition task with dynamic (video) stimuli compared to a task using only static (photograph) stimuli and compared performances on both tasks to healthy control participants. In a within-subjects study, 21 PD participants and 20 age-matched healthy controls performed both static and dynamic emotion recognition tasks. The authors used a 2-way analysis of variance (controlling for individual participant variance) to determine the effect of group (PD, control) on emotion recognition performance in static and dynamic facial recognition tasks. Groups did not significantly differ in their performances on the static and dynamic tasks; however, the trend was suggestive that PD participants performed worse than controls. PD participants may have subtle emotion recognition deficits that are not ameliorated by the addition of contextual cues, similar to those found in everyday scenarios. Consistent with previous literature, the results suggest that PD participants may have underlying emotion recognition deficits, which may impact their social functioning. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

A new pre-classification method based on associative matching method

NASA Astrophysics Data System (ADS)

Katsuyama, Yutaka; Minagawa, Akihiro; Hotta, Yoshinobu; Omachi, Shinichiro; Kato, Nei

2010-01-01

Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical Character Recognition (OCR) systems. To shorten processing time, recognition is usually split into separate preclassification and recognition stages. For high overall recognition performance, the pre-classification stage must both have very high classification accuracy and return only a small number of putative character categories for further processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The associative matching (AM) method has often been used for fast pre-classification, because its use of a hash table and reliance solely on logical bit operations to select categories makes it highly efficient. However, redundant certain level of redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data on each axis and therefore does not take account of the distribution of the data. We propose a modified associative matching method that satisfies the performance criteria described above but in a fraction of the time by modifying the hash table to reflect the underlying distribution of training characters. Furthermore, we show that our approach outperforms pre-classification by clustering, ANN and conventional AM in terms of classification accuracy, discriminative power and speed. Compared to conventional associative matching, the proposed approach results in a 47% reduction in total processing time across an evaluation test set comprising 116,528 Japanese character images.

Effective Principal Behaviors.

ERIC Educational Resources Information Center

Research for Better Schools, Inc., Philadelphia, PA.

This paper summarizes research findings related to the performance behaviors of effective school principals. It identifies eight characteristics of effective schools: (1) schoolwide measurement and recognition of academic success; (2) high emphasis on curriculum articulation; (3) support for instructional tasks; (4) high expectations and clear…

Affective attention under cognitive load: reduced emotional biases but emergent anxiety-related costs to inhibitory control

PubMed Central

Berggren, Nick; Richards, Anne; Taylor, Joseph; Derakshan, Nazanin

2013-01-01

Trait anxiety is associated with deficits in attentional control, particularly in the ability to inhibit prepotent responses. Here, we investigated this effect while varying the level of cognitive load in a modified antisaccade task that employed emotional facial expressions (neutral, happy, and angry) as targets. Load was manipulated using a secondary auditory task requiring recognition of tones (low load), or recognition of specific tone pitch (high load). Results showed that load increased antisaccade latencies on trials where gaze toward face stimuli should be inhibited. This effect was exacerbated for high anxious individuals. Emotional expression also modulated task performance on antisaccade trials for both high and low anxious participants under low cognitive load, but did not influence performance under high load. Collectively, results (1) suggest that individuals reporting high levels of anxiety are particularly vulnerable to the effects of cognitive load on inhibition, and (2) support recent evidence that loading cognitive processes can reduce emotional influences on attention and cognition. PMID:23717273

Experience moderates overlap between object and face recognition, suggesting a common ability

PubMed Central

Gauthier, Isabel; McGugin, Rankin W.; Richler, Jennifer J.; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E.

2014-01-01

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. PMID:24993021

Experience moderates overlap between object and face recognition, suggesting a common ability.

PubMed

Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

2014-07-03

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. © 2014 ARVO.

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits

PubMed Central

Actis-Grosso, Rossana; Bossi, Francesco; Ricciardelli, Paola

2015-01-01

We investigated whether the type of stimulus (pictures of static faces vs. body motion) contributes differently to the recognition of emotions. The performance (accuracy and response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20 young adults (16 males) with either High Autistic Traits or with High Functioning Autism Spectrum Disorder (HAT group) was compared in the recognition of four emotions (Happiness, Anger, Fear, and Sadness) either shown in static faces or conveyed by moving body patch-light displays (PLDs). Overall, HAT individuals were as accurate as LAT ones in perceiving emotions both with faces and with PLDs. Moreover, they correctly described non-emotional actions depicted by PLDs, indicating that they perceived the motion conveyed by the PLDs per se. For LAT participants, happiness proved to be the easiest emotion to be recognized: in line with previous studies we found a happy face advantage for faces, which for the first time was also found for bodies (happy body advantage). Furthermore, LAT participants recognized sadness better by static faces and fear by PLDs. This advantage for motion kinematics in the recognition of fear was not present in HAT participants, suggesting that (i) emotion recognition is not generally impaired in HAT individuals, (ii) the cues exploited for emotion recognition by LAT and HAT groups are not always the same. These findings are discussed against the background of emotional processing in typically and atypically developed individuals. PMID:26557101

Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits.

PubMed

Actis-Grosso, Rossana; Bossi, Francesco; Ricciardelli, Paola

2015-01-01

We investigated whether the type of stimulus (pictures of static faces vs. body motion) contributes differently to the recognition of emotions. The performance (accuracy and response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20 young adults (16 males) with either High Autistic Traits or with High Functioning Autism Spectrum Disorder (HAT group) was compared in the recognition of four emotions (Happiness, Anger, Fear, and Sadness) either shown in static faces or conveyed by moving body patch-light displays (PLDs). Overall, HAT individuals were as accurate as LAT ones in perceiving emotions both with faces and with PLDs. Moreover, they correctly described non-emotional actions depicted by PLDs, indicating that they perceived the motion conveyed by the PLDs per se. For LAT participants, happiness proved to be the easiest emotion to be recognized: in line with previous studies we found a happy face advantage for faces, which for the first time was also found for bodies (happy body advantage). Furthermore, LAT participants recognized sadness better by static faces and fear by PLDs. This advantage for motion kinematics in the recognition of fear was not present in HAT participants, suggesting that (i) emotion recognition is not generally impaired in HAT individuals, (ii) the cues exploited for emotion recognition by LAT and HAT groups are not always the same. These findings are discussed against the background of emotional processing in typically and atypically developed individuals.

Relationship between listeners' nonnative speech recognition and categorization abilities

PubMed Central

Atagi, Eriko; Bent, Tessa

2015-01-01

Enhancement of the perceptual encoding of talker characteristics (indexical information) in speech can facilitate listeners' recognition of linguistic content. The present study explored this indexical-linguistic relationship in nonnative speech processing by examining listeners' performance on two tasks: nonnative accent categorization and nonnative speech-in-noise recognition. Results indicated substantial variability across listeners in their performance on both the accent categorization and nonnative speech recognition tasks. Moreover, listeners' accent categorization performance correlated with their nonnative speech-in-noise recognition performance. These results suggest that having more robust indexical representations for nonnative accents may allow listeners to more accurately recognize the linguistic content of nonnative speech. PMID:25618098

The 48-Pictures Test: a two-alternative forced-choice recognition test for the detection of malingering.

PubMed

Chouinard, M J; Rouleau, I

1997-11-01

We tested the validity of the 48-Pictures Test, a 2-alternative forced-choice recognition test, in detecting exaggerated memory impairments. This test maximizes subjective difficulty, through a large number of stimuli and shows minimal objective difficulty. We compared 17 suspected malingerers to 39 patients with memory impairments (6 amnesic, 15 frontal lobe dysfunctions, 18 other etiologies), and 17 normal adults instructed to simulate malingering on three memory tests: the 48-Pictures Test, the Rey Auditory Verbal Learning Test (RAVLT), and the Rey Complex Figure Test (RCFT). On the 48-Pictures Test, the clinical groups showed good recognition performance (amnesics: 85%; frontal dysfunction: 94%; other memory impairments: 97%), whereas the two simulator groups showed a poor performance (suspected malingerers: 62% correct; volunteer simulators 68% correct). The two other tests did not show a high degree of discrimination between the clinical groups and the simulator groups, except in 2 measures: the 2 simulator groups tended to show a performance decrement from the last recall trial to immediate recognition of the RAVLT and also performed better than the clinical groups on the immediate recall of the RCFT. A discriminant analysis with the latter 2 measures and the 48-Pictures Test correctly classified 96% of the participants. These results suggest that the 48-Pictures Test is a useful tool for the detection of possible simulated memory impairment and that when combined to the RAVLT recall-recognition difference score and to the immediate recall score on the RCFT can provide strong evidence of exaggerated memory impairment.

Sound Processing Features for Speaker-Dependent and Phrase-Independent Emotion Recognition in Berlin Database

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Christos Nikolaos; Vovoli, Eftichia

An emotion recognition framework based on sound processing could improve services in human-computer interaction. Various quantitative speech features obtained from sound processing of acting speech were tested, as to whether they are sufficient or not to discriminate between seven emotions. Multilayered perceptrons were trained to classify gender and emotions on the basis of a 24-input vector, which provide information about the prosody of the speaker over the entire sentence using statistics of sound features. Several experiments were performed and the results were presented analytically. Emotion recognition was successful when speakers and utterances were “known” to the classifier. However, severe misclassifications occurred during the utterance-independent framework. At least, the proposed feature vector achieved promising results for utterance-independent recognition of high- and low-arousal emotions.

Associations between facial emotion recognition and young adolescents’ behaviors in bullying

PubMed Central

Gini, Gianluca; Altoè, Gianmarco

2017-01-01

This study investigated whether different behaviors young adolescents can act during bullying episodes were associated with their ability to recognize morphed facial expressions of the six basic emotions, expressed at high and low intensity. The sample included 117 middle-school students (45.3% girls; mean age = 12.4 years) who filled in a peer nomination questionnaire and individually performed a computerized emotion recognition task. Bayesian generalized mixed-effects models showed a complex picture, in which type and intensity of emotions, students’ behavior and gender interacted in explaining recognition accuracy. Results were discussed with a particular focus on negative emotions and suggesting a “neutral” nature of emotion recognition ability, which does not necessarily lead to moral behavior but can also be used for pursuing immoral goals. PMID:29131871

Kruskal-Wallis-based computationally efficient feature selection for face recognition.

PubMed

Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz

2014-01-01

Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

Young children's coding and storage of visual and verbal material.

PubMed

Perlmutter, M; Myers, N A

1975-03-01

36 preschool children (mean age 4.2 years) were each tested on 3 recognition memory lists differing in test mode (visual only, verbal only, combined visual-verbal). For one-third of the children, original list presentation was visual only, for another third, presentation was verbal only, and the final third received combined visual-verbal presentation. The subjects generally performed at a high level of correct responding. Verbal-only presentation resulted in less correct recognition than did either visual-only or combined visual-verbal presentation. However, because performances under both visual-only and combined visual-verbal presentation were statistically comparable, and a high level of spontaneous labeling was observed when items were presented only visually, a dual-processing conceptualization of memory in 4-year-olds was suggested.

Are there differential deficits in facial emotion recognition between paranoid and non-paranoid schizophrenia? A signal detection analysis.

PubMed

Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

2013-10-30

This study assessed facial emotion recognition abilities in subjects with paranoid and non-paranoid schizophrenia (NPS) using signal detection theory. We explore the differential deficits in facial emotion recognition in 44 paranoid patients with schizophrenia (PS) and 30 non-paranoid patients with schizophrenia (NPS), compared to 80 healthy controls. We used morphed faces with different intensities of emotion and computed the sensitivity index (d') of each emotion. The results showed that performance differed between the schizophrenia and healthy controls groups in the recognition of both negative and positive affects. The PS group performed worse than the healthy controls group but better than the NPS group in overall performance. Performance differed between the NPS and healthy controls groups in the recognition of all basic emotions and neutral faces; between the PS and healthy controls groups in the recognition of angry faces; and between the PS and NPS groups in the recognition of happiness, anger, sadness, disgust, and neutral affects. The facial emotion recognition impairment in schizophrenia may reflect a generalized deficit rather than a negative-emotion specific deficit. The PS group performed worse than the control group, but better than the NPS group in facial expression recognition, with differential deficits between PS and NPS patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Oxygen and carbon dioxide sensing

NASA Technical Reports Server (NTRS)

Ren, Fan (Inventor); Pearton, Stephen John (Inventor)

2012-01-01

A high electron mobility transistor (HEMT) capable of performing as a CO.sub.2 or O.sub.2 sensor is disclosed, hi one implementation, a polymer solar cell can be connected to the HEMT for use in an infrared detection system. In a second implementation, a selective recognition layer can be provided on a gate region of the HEMT. For carbon dioxide sensing, the selective recognition layer can be, in one example, PEI/starch. For oxygen sensing, the selective recognition layer can be, in one example, indium zinc oxide (IZO). In one application, the HEMTs can be used for the detection of carbon dioxide and oxygen in exhaled breath or blood.

Anomalous subjective experience and psychosis risk in young depressed patients.

PubMed

Szily, Erika; Kéri, Szabolcs

2009-01-01

Help-seeking young people often display depressive symptoms. In some patients, these symptoms may co-exist with clinically high-risk mental states for psychosis. The aim of this study was to determine differences in subjective experience and social perception in young depressed patients with and without psychosis risk. Participants were 68 young persons with major depressive disorder. Twenty-six patients also met the criteria of attenuated or brief limited intermittent psychotic symptoms according to the Comprehensive Assessment of At Risk Mental States (CAARMS) criteria. Subjective experiences were assessed with the Bonn Scale for the Assessment of Basic Symptoms (BSABS). Recognition of complex social emotions and mental states was assessed using the 'Reading the Mind in the Eyes' test. Perplexity, self-disorder, and diminished affectivity significantly predicted psychosis risk. Depressed patients without psychosis risk displayed impaired recognition performance for negative social emotions, whereas patients with psychosis risk were also impaired in the recognition of cognitive expressions. In the high-risk group, self-disorder was associated with impaired recognition of facial expressions. These results suggest that anomalous subjective experience and impaired recognition of complex emotions may differentiate between young depressed patients with and without psychosis risk. 2009 S. Karger AG, Basel.

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Famous face recognition, face matching, and extraversion.

PubMed

Lander, Karen; Poyarekar, Siddhi

2015-01-01

It has been previously established that extraverts who are skilled at interpersonal interaction perform significantly better than introverts on a face-specific recognition memory task. In our experiment we further investigate the relationship between extraversion and face recognition, focusing on famous face recognition and face matching. Results indicate that more extraverted individuals perform significantly better on an upright famous face recognition task and show significantly larger face inversion effects. However, our results did not find an effect of extraversion on face matching or inverted famous face recognition.

The Effect of Lexical Content on Dichotic Speech Recognition in Older Adults.

PubMed

Findlen, Ursula M; Roup, Christina M

2016-01-01

Age-related auditory processing deficits have been shown to negatively affect speech recognition for older adult listeners. In contrast, older adults gain benefit from their ability to make use of semantic and lexical content of the speech signal (i.e., top-down processing), particularly in complex listening situations. Assessment of auditory processing abilities among aging adults should take into consideration semantic and lexical content of the speech signal. The purpose of this study was to examine the effects of lexical and attentional factors on dichotic speech recognition performance characteristics for older adult listeners. A repeated measures design was used to examine differences in dichotic word recognition as a function of lexical and attentional factors. Thirty-five older adults (61-85 yr) with sensorineural hearing loss participated in this study. Dichotic speech recognition was evaluated using consonant-vowel-consonant (CVC) word and nonsense CVC syllable stimuli administered in the free recall, directed recall right, and directed recall left response conditions. Dichotic speech recognition performance for nonsense CVC syllables was significantly poorer than performance for CVC words. Dichotic recognition performance varied across response condition for both stimulus types, which is consistent with previous studies on dichotic speech recognition. Inspection of individual results revealed that five listeners demonstrated an auditory-based left ear deficit for one or both stimulus types. Lexical content of stimulus materials affects performance characteristics for dichotic speech recognition tasks in the older adult population. The use of nonsense CVC syllable material may provide a way to assess dichotic speech recognition performance while potentially lessening the effects of lexical content on performance (i.e., measuring bottom-up auditory function both with and without top-down processing). American Academy of Audiology.

High Class

ERIC Educational Resources Information Center

Waldecker, Mark

2005-01-01

Education administrators make buying decisions for furniture based on many factors. Cost, durability, functionality, safety and aesthetics represent just a few. Those issues always will be important, but gaining greater recognition in recent years has been the role furniture plays in creating positive, high-performance learning environments. The…

Speech recognition systems on the Cell Broadband Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Jones, H; Vaidya, S

In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousandsmore » of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.« less

The Doors and People Test: The Effect of Frontal Lobe Lesions on Recall and Recognition Memory Performance

PubMed Central

2016-01-01

Objective: Memory deficits in patients with frontal lobe lesions are most apparent on free recall tasks that require the selection, initiation, and implementation of retrieval strategies. The effect of frontal lesions on recognition memory performance is less clear with some studies reporting recognition memory impairments but others not. The majority of these studies do not directly compare recall and recognition within the same group of frontal patients, assessing only recall or recognition memory performance. Other studies that do compare recall and recognition in the same frontal group do not consider recall or recognition tests that are comparable for difficulty. Recognition memory impairments may not be reported because recognition memory tasks are less demanding. Method: This study aimed to investigate recall and recognition impairments in the same group of 47 frontal patients and 78 healthy controls. The Doors and People Test was administered as a neuropsychological test of memory as it assesses both verbal and visual recall and recognition using subtests that are matched for difficulty. Results: Significant verbal and visual recall and recognition impairments were found in the frontal patients. Conclusion: These results demonstrate that when frontal patients are assessed on recall and recognition memory tests of comparable difficulty, memory impairments are found on both types of episodic memory test. PMID:26752123

The Doors and People Test: The effect of frontal lobe lesions on recall and recognition memory performance.

PubMed

MacPherson, Sarah E; Turner, Martha S; Bozzali, Marco; Cipolotti, Lisa; Shallice, Tim

2016-03-01

Memory deficits in patients with frontal lobe lesions are most apparent on free recall tasks that require the selection, initiation, and implementation of retrieval strategies. The effect of frontal lesions on recognition memory performance is less clear with some studies reporting recognition memory impairments but others not. The majority of these studies do not directly compare recall and recognition within the same group of frontal patients, assessing only recall or recognition memory performance. Other studies that do compare recall and recognition in the same frontal group do not consider recall or recognition tests that are comparable for difficulty. Recognition memory impairments may not be reported because recognition memory tasks are less demanding. This study aimed to investigate recall and recognition impairments in the same group of 47 frontal patients and 78 healthy controls. The Doors and People Test was administered as a neuropsychological test of memory as it assesses both verbal and visual recall and recognition using subtests that are matched for difficulty. Significant verbal and visual recall and recognition impairments were found in the frontal patients. These results demonstrate that when frontal patients are assessed on recall and recognition memory tests of comparable difficulty, memory impairments are found on both types of episodic memory test. (c) 2016 APA, all rights reserved).

Aging and solid shape recognition: Vision and haptics.

PubMed

Norman, J Farley; Cheeseman, Jacob R; Adkins, Olivia C; Cox, Andrea G; Rogers, Connor E; Dowell, Catherine J; Baxter, Michael W; Norman, Hideko F; Reyes, Cecia M

2015-10-01

The ability of 114 younger and older adults to recognize naturally-shaped objects was evaluated in three experiments. The participants viewed or haptically explored six randomly-chosen bell peppers (Capsicum annuum) in a study session and were later required to judge whether each of twelve bell peppers was "old" (previously presented during the study session) or "new" (not presented during the study session). When recognition memory was tested immediately after study, the younger adults' (Experiment 1) performance for vision and haptics was identical when the individual study objects were presented once. Vision became superior to haptics, however, when the individual study objects were presented multiple times. When 10- and 20-min delays (Experiment 2) were inserted in between study and test sessions, no significant differences occurred between vision and haptics: recognition performance in both modalities was comparable. When the recognition performance of older adults was evaluated (Experiment 3), a negative effect of age was found for visual shape recognition (younger adults' overall recognition performance was 60% higher). There was no age effect, however, for haptic shape recognition. The results of the present experiments indicate that the visual recognition of natural object shape is different from haptic recognition in multiple ways: visual shape recognition can be superior to that of haptics and is affected by aging, while haptic shape recognition is less accurate and unaffected by aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Impact of Age, Background Noise, Semantic Ambiguity, and Hearing Loss on Recognition Memory for Spoken Sentences.

PubMed

Koeritzer, Margaret A; Rogers, Chad S; Van Engen, Kristin J; Peelle, Jonathan E

2018-03-15

The goal of this study was to determine how background noise, linguistic properties of spoken sentences, and listener abilities (hearing sensitivity and verbal working memory) affect cognitive demand during auditory sentence comprehension. We tested 30 young adults and 30 older adults. Participants heard lists of sentences in quiet and in 8-talker babble at signal-to-noise ratios of +15 dB and +5 dB, which increased acoustic challenge but left the speech largely intelligible. Half of the sentences contained semantically ambiguous words to additionally manipulate cognitive challenge. Following each list, participants performed a visual recognition memory task in which they viewed written sentences and indicated whether they remembered hearing the sentence previously. Recognition memory (indexed by d') was poorer for acoustically challenging sentences, poorer for sentences containing ambiguous words, and differentially poorer for noisy high-ambiguity sentences. Similar patterns were observed for Z-transformed response time data. There were no main effects of age, but age interacted with both acoustic clarity and semantic ambiguity such that older adults' recognition memory was poorer for acoustically degraded high-ambiguity sentences than the young adults'. Within the older adult group, exploratory correlation analyses suggested that poorer hearing ability was associated with poorer recognition memory for sentences in noise, and better verbal working memory was associated with better recognition memory for sentences in noise. Our results demonstrate listeners' reliance on domain-general cognitive processes when listening to acoustically challenging speech, even when speech is highly intelligible. Acoustic challenge and semantic ambiguity both reduce the accuracy of listeners' recognition memory for spoken sentences. https://doi.org/10.23641/asha.5848059.

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise

PubMed Central

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of “verbal abilities” that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18–35 years) and 22 older (60–78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults’ poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access. PMID:27458400

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise.

PubMed

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of "verbal abilities" that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18-35 years) and 22 older (60-78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults' poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access.

4 CFR 2.4 - Merit system principles.

Code of Federal Regulations, 2011 CFR

2011-01-01

... recognition should be provided for excellence in performance. (4) All employees should maintain high standards... education and training in cases in which such education and training would result in better organizational...

A reciprocal model of face recognition and autistic traits: evidence from an individual differences perspective.

PubMed

Halliday, Drew W R; MacDonald, Stuart W S; Scherf, K Suzanne; Sherf, Suzanne K; Tanaka, James W

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

A Reciprocal Model of Face Recognition and Autistic Traits: Evidence from an Individual Differences Perspective

PubMed Central

Halliday, Drew W. R.; MacDonald, Stuart W. S.; Sherf, Suzanne K.; Tanaka, James W.

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals. PMID:24853862

Image enhancement and advanced information extraction techniques for ERTS-1 data

NASA Technical Reports Server (NTRS)

Malila, W. A. (Principal Investigator); Nalepka, R. F.; Sarno, J. E.

1975-01-01

The author has identified the following significant results. It was demonstrated and concluded that: (1) the atmosphere has significant effects on ERTS MSS data which can seriously degrade recognition performance; (2) the application of selected signature extension techniques serve to reduce the deleterious effects of both the atmosphere and changing ground conditions on recognition performance; and (3) a proportion estimation algorithm for overcoming problems in acreage estimation accuracy resulting from the coarse spatial resolution of the ERTS MSS, was able to significantly improve acreage estimation accuracy over that achievable by conventional techniques, especially for high contrast targets such as lakes and ponds.

Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model.

PubMed

Polur, Prasad D; Miller, Gerald E

2005-01-01

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients, requires a robust technique that can handle conditions of very high variability and limited training data. In this study, a hidden Markov model (HMM) was constructed and conditions investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system intended to act as an assistive/control tool. In particular, we investigated the effect of high-frequency spectral components on the recognition rate of the system to determine if they contributed useful additional information to the system. A small-size vocabulary spoken by three cerebral palsy subjects was chosen. Mel-frequency cepstral coefficients extracted with the use of 15 ms frames served as training input to an ergodic HMM setup. Subsequent results demonstrated that no significant useful information was available to the system for enhancing its ability to discriminate dysarthric speech above 5.5 kHz in the current set of dysarthric data. The level of variability in input dysarthric speech patterns limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor-impaired individuals such as cerebral palsy subjects holds sufficient promise.

Impaired recognition of faces and objects in dyslexia: Evidence for ventral stream dysfunction?

PubMed

Sigurdardottir, Heida Maria; Ívarsson, Eysteinn; Kristinsdóttir, Kristjana; Kristjánsson, Árni

2015-09-01

The objective of this study was to establish whether or not dyslexics are impaired at the recognition of faces and other complex nonword visual objects. This would be expected based on a meta-analysis revealing that children and adult dyslexics show functional abnormalities within the left fusiform gyrus, a brain region high up in the ventral visual stream, which is thought to support the recognition of words, faces, and other objects. 20 adult dyslexics (M = 29 years) and 20 matched typical readers (M = 29 years) participated in the study. One dyslexic-typical reader pair was excluded based on Adult Reading History Questionnaire scores and IS-FORM reading scores. Performance was measured on 3 high-level visual processing tasks: the Cambridge Face Memory Test, the Vanderbilt Holistic Face Processing Test, and the Vanderbilt Expertise Test. People with dyslexia are impaired in their recognition of faces and other visually complex objects. Their holistic processing of faces appears to be intact, suggesting that dyslexics may instead be specifically impaired at part-based processing of visual objects. The difficulty that people with dyslexia experience with reading might be the most salient manifestation of a more general high-level visual deficit. (c) 2015 APA, all rights reserved).

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Temporal lobe structures and facial emotion recognition in schizophrenia patients and nonpsychotic relatives.

PubMed

Goghari, Vina M; Macdonald, Angus W; Sponheim, Scott R

2011-11-01

Temporal lobe abnormalities and emotion recognition deficits are prominent features of schizophrenia and appear related to the diathesis of the disorder. This study investigated whether temporal lobe structural abnormalities were associated with facial emotion recognition deficits in schizophrenia and related to genetic liability for the disorder. Twenty-seven schizophrenia patients, 23 biological family members, and 36 controls participated. Several temporal lobe regions (fusiform, superior temporal, middle temporal, amygdala, and hippocampus) previously associated with face recognition in normative samples and found to be abnormal in schizophrenia were evaluated using volumetric analyses. Participants completed a facial emotion recognition task and an age recognition control task under time-limited and self-paced conditions. Temporal lobe volumes were tested for associations with task performance. Group status explained 23% of the variance in temporal lobe volume. Left fusiform gray matter volume was decreased by 11% in patients and 7% in relatives compared with controls. Schizophrenia patients additionally exhibited smaller hippocampal and middle temporal volumes. Patients were unable to improve facial emotion recognition performance with unlimited time to make a judgment but were able to improve age recognition performance. Patients additionally showed a relationship between reduced temporal lobe gray matter and poor facial emotion recognition. For the middle temporal lobe region, the relationship between greater volume and better task performance was specific to facial emotion recognition and not age recognition. Because schizophrenia patients exhibited a specific deficit in emotion recognition not attributable to a generalized impairment in face perception, impaired emotion recognition may serve as a target for interventions.

A Palmprint Recognition Algorithm Using Phase-Only Correlation

NASA Astrophysics Data System (ADS)

Ito, Koichi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system

NASA Astrophysics Data System (ADS)

Poinsot, Audrey; Yang, Fan; Brost, Vincent

2011-02-01

Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. We propose a contactless biometric system that combines two modalities: palmprint and face. Hardware implementations are proposed on the Texas Instrument Digital Signal Processor and Xilinx Field-Programmable Gate Array (FPGA) platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested first using a bimodal database of 130 subjects that we designed (uB database), and then two common public biometric databases (AR for face and PolyU for palmprint). High performance has been obtained for recognition and verification purpose: a recognition rate of 97.49% with AR-PolyU database and an equal error rate of 1.10% on the uB database using only two training samples per subject have been obtained. Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA). We show the feasibility of a robust and efficient multimodal hardware biometric system that offers several advantages, such as user-friendliness and flexibility.

Benefits of using culturally unfamiliar stimuli in ambiguous emotion identification: A cross-cultural study.

PubMed

Koelkebeck, Katja; Kohl, Waldemar; Luettgenau, Julia; Triantafillou, Susanna; Ohrmann, Patricia; Satoh, Shinji; Minoshita, Seiko

2015-07-30

A novel emotion recognition task that employs photos of a Japanese mask representing a highly ambiguous stimulus was evaluated. As non-Asians perceive and/or label emotions differently from Asians, we aimed to identify patterns of task-performance in non-Asian healthy volunteers with a view to future patient studies. The Noh mask test was presented to 42 adult German participants. Reaction times and emotion attribution patterns were recorded. To control for emotion identification abilities, a standard emotion recognition task was used among others. Questionnaires assessed personality traits. Finally, results were compared to age- and gender-matched Japanese volunteers. Compared to other tasks, German participants displayed slowest reaction times on the Noh mask test, indicating higher demands of ambiguous emotion recognition. They assigned more positive emotions to the mask than Japanese volunteers, demonstrating culture-dependent emotion identification patterns. As alexithymic and anxious traits were associated with slower reaction times, personality dimensions impacted on performance, as well. We showed an advantage of ambiguous over conventional emotion recognition tasks. Moreover, we determined emotion identification patterns in Western individuals impacted by personality dimensions, suggesting performance differences in clinical samples. Due to its properties, the Noh mask test represents a promising tool in the differential diagnosis of psychiatric disorders, e.g. schizophrenia. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

PubMed

Zhao, Jianwei; Lv, Yongbiao; Zhou, Zhenghua; Cao, Feilong

2017-10-01

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

The effect of inversion on face recognition in adults with autism spectrum disorder.

PubMed

Hedley, Darren; Brewer, Neil; Young, Robyn

2015-05-01

Face identity recognition has widely been shown to be impaired in individuals with autism spectrum disorders (ASD). In this study we examined the influence of inversion on face recognition in 26 adults with ASD and 33 age and IQ matched controls. Participants completed a recognition test comprising upright and inverted faces. Participants with ASD performed worse than controls on the recognition task but did not show an advantage for inverted face recognition. Both groups directed more visual attention to the eye than the mouth region and gaze patterns were not found to be associated with recognition performance. These results provide evidence of a normal effect of inversion on face recognition in adults with ASD.

The beneficial effect of oxytocin on avoidance-related facial emotion recognition depends on early life stress experience.

PubMed

Feeser, Melanie; Fan, Yan; Weigand, Anne; Hahn, Adam; Gärtner, Matti; Aust, Sabine; Böker, Heinz; Bajbouj, Malek; Grimm, Simone

2014-12-01

Previous studies have shown that oxytocin (OXT) enhances social cognitive processes. It has also been demonstrated that OXT does not uniformly facilitate social cognition. The effects of OXT administration strongly depend on the exposure to stressful experiences in early life. Emotional facial recognition is crucial for social cognition. However, no study has yet examined how the effects of OXT on the ability to identify emotional faces are altered by early life stress (ELS) experiences. Given the role of OXT in modulating social motivational processes, we specifically aimed to investigate its effects on the recognition of approach- and avoidance-related facial emotions. In a double-blind, between-subjects, placebo-controlled design, 82 male participants performed an emotion recognition task with faces taken from the "Karolinska Directed Emotional Faces" set. We clustered the six basic emotions along the dimensions approach (happy, surprise, anger) and avoidance (fear, sadness, disgust). ELS was assessed with the Childhood Trauma Questionnaire (CTQ). Our results showed that OXT improved the ability to recognize avoidance-related emotional faces as compared to approach-related emotional faces. Whereas the performance for avoidance-related emotions in participants with higher ELS scores was comparable in both OXT and placebo condition, OXT enhanced emotion recognition in participants with lower ELS scores. Independent of OXT administration, we observed increased emotion recognition for avoidance-related faces in participants with high ELS scores. Our findings suggest that the investigation of OXT on social recognition requires a broad approach that takes ELS experiences as well as motivational processes into account.

The Seductive Details Effect in Technology-Delivered Instruction

ERIC Educational Resources Information Center

Towler, Annette; Kraiger, Kurt; Sitzmann, Traci; Van Overberghe, Courtney; Cruz, Jaime; Ronen, Eyal; Stewart, David

2008-01-01

Seductive details are highly interesting information tangential to course objectives. The inclusion of seductive details generally harms performance on recall tests, but few studies have used multimedia training or investigated effects on performance on recognition tests or transfer tasks. We conducted two studies using computer-based training,…

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

PubMed

Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

2018-03-01

Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.

Effects of emotion and reward motivation on neural correlates of episodic memory encoding: a PET study.

PubMed

Shigemune, Yayoi; Abe, Nobuhito; Suzuki, Maki; Ueno, Aya; Mori, Etsuro; Tashiro, Manabu; Itoh, Masatoshi; Fujii, Toshikatsu

2010-05-01

It is known that emotion and reward motivation promote long-term memory formation. It remains unclear, however, how and where emotion and reward are integrated during episodic memory encoding. In the present study, subjects were engaged in intentional encoding of photographs under four different conditions that were made by combining two factors (emotional valence, negative or neutral; and monetary reward value, high or low for subsequent successful recognition) during H2 15O positron emission tomography (PET) scanning. As for recognition performance, we found significant main effects of emotional valence (negative>neutral) and reward value (high value>low value), without an interaction between the two factors. Imaging data showed that the left amygdala was activated during the encoding conditions of negative pictures relative to neutral pictures, and the left orbitofrontal cortex was activated during the encoding conditions of high reward pictures relative to low reward pictures. In addition, conjunction analysis of these two main effects detected right hippocampal activation. Although we could not find correlations between recognition performance and activity of these three regions, we speculate that the right hippocampus may integrate the effects of emotion (processed in the amygdala) and monetary reward (processed in the orbitofrontal cortex) on episodic memory encoding. 2010 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Line fitting based feature extraction for object recognition

NASA Astrophysics Data System (ADS)

Li, Bing

2014-06-01

Image feature extraction plays a significant role in image based pattern applications. In this paper, we propose a new approach to generate hierarchical features. This new approach applies line fitting to adaptively divide regions based upon the amount of information and creates line fitting features for each subsequent region. It overcomes the feature wasting drawback of the wavelet based approach and demonstrates high performance in real applications. For gray scale images, we propose a diffusion equation approach to map information-rich pixels (pixels near edges and ridge pixels) into high values, and pixels in homogeneous regions into small values near zero that form energy map images. After the energy map images are generated, we propose a line fitting approach to divide regions recursively and create features for each region simultaneously. This new feature extraction approach is similar to wavelet based hierarchical feature extraction in which high layer features represent global characteristics and low layer features represent local characteristics. However, the new approach uses line fitting to adaptively focus on information-rich regions so that we avoid the feature waste problems of the wavelet approach in homogeneous regions. Finally, the experiments for handwriting word recognition show that the new method provides higher performance than the regular handwriting word recognition approach.

Data Intensive Systems (DIS) Benchmark Performance Summary

DTIC Science & Technology

2003-08-01

models assumed by today’s conventional architectures. Such applications include model- based Automatic Target Recognition (ATR), synthetic aperture...radar (SAR) codes, large scale dynamic databases/battlefield integration, dynamic sensor- based processing, high-speed cryptanalysis, high speed...distributed interactive and data intensive simulations, data-oriented problems characterized by pointer- based and other highly irregular data structures

Recognition of facial expressions and prosodic cues with graded emotional intensities in adults with Asperger syndrome.

PubMed

Doi, Hirokazu; Fujisawa, Takashi X; Kanai, Chieko; Ohta, Haruhisa; Yokoi, Hideki; Iwanami, Akira; Kato, Nobumasa; Shinohara, Kazuyuki

2013-09-01

This study investigated the ability of adults with Asperger syndrome to recognize emotional categories of facial expressions and emotional prosodies with graded emotional intensities. The individuals with Asperger syndrome showed poorer recognition performance for angry and sad expressions from both facial and vocal information. The group difference in facial expression recognition was prominent for stimuli with low or intermediate emotional intensities. In contrast to this, the individuals with Asperger syndrome exhibited lower recognition accuracy than typically-developed controls mainly for emotional prosody with high emotional intensity. In facial expression recognition, Asperger and control groups showed an inversion effect for all categories. The magnitude of this effect was less in the Asperger group for angry and sad expressions, presumably attributable to reduced recruitment of the configural mode of face processing. The individuals with Asperger syndrome outperformed the control participants in recognizing inverted sad expressions, indicating enhanced processing of local facial information representing sad emotion. These results suggest that the adults with Asperger syndrome rely on modality-specific strategies in emotion recognition from facial expression and prosodic information.

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Line-based logo recognition through a web-camera

NASA Astrophysics Data System (ADS)

Chen, Xiaolu; Wang, Yangsheng; Feng, Xuetao

2007-11-01

Logo recognition has gained much development in the document retrieval and shape analysis domain. As human computer interaction becomes more and more popular, the logo recognition through a web-camera is a promising technology in view of application. But for practical application, the study of logo recognition in real scene is much more difficult than the work in clear scene. To cope with the need, we make some improvements on conventional method. First, moment information is used to calculate the test image's orientation angle, which is used to normalize the test image. Second, the main structure of the test image, which is represented by lines patterns, is acquired and modified Hausdorff distance is employed to match the image and each of the existing templates. The proposed method, which is invariant to scale and rotation, gives good result and can work at real-time. The main contribution of this paper is that some improvements are introduced into the exiting recognition framework which performs much better than the original one. Besides, we have built a highly successful logo recognition system using our improved method.

Finger vein recognition based on personalized weight maps.

PubMed

Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-09-10

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition.

Finger Vein Recognition Based on Personalized Weight Maps

PubMed Central

Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-01-01

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. PMID:24025556

Degraded Impairment of Emotion Recognition in Parkinson's Disease Extends from Negative to Positive Emotions.

PubMed

Lin, Chia-Yao; Tien, Yi-Min; Huang, Jong-Tsun; Tsai, Chon-Haw; Hsu, Li-Chuan

2016-01-01

Because of dopaminergic neurodegeneration, patients with Parkinson's disease (PD) show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger) and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients' ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions.

Degraded Impairment of Emotion Recognition in Parkinson's Disease Extends from Negative to Positive Emotions

PubMed Central

Tien, Yi-Min; Huang, Jong-Tsun

2016-01-01

Because of dopaminergic neurodegeneration, patients with Parkinson's disease (PD) show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger) and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients' ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions. PMID:27555668

Target recognition for ladar range image using slice image

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Wang, Liang

2015-12-01

A shape descriptor and a complete shape-based recognition system using slice images as geometric feature descriptor for ladar range images are introduced. A slice image is a two-dimensional image generated by three-dimensional Hough transform and the corresponding mathematical transformation. The system consists of two processes, the model library construction and recognition. In the model library construction process, a series of range images are obtained after the model object is sampled at preset attitude angles. Then, all the range images are converted into slice images. The number of slice images is reduced by clustering analysis and finding a representation to reduce the size of the model library. In the recognition process, the slice image of the scene is compared with the slice image in the model library. The recognition results depend on the comparison. Simulated ladar range images are used to analyze the recognition and misjudgment rates, and comparison between the slice image representation method and moment invariants representation method is performed. The experimental results show that whether in conditions without noise or with ladar noise, the system has a high recognition rate and low misjudgment rate. The comparison experiment demonstrates that the slice image has better representation ability than moment invariants.

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

Validation of a short-term memory test for the recognition of people and faces.

PubMed

Leyk, D; Sievert, A; Heiss, A; Gorges, W; Ridder, D; Alexander, T; Wunderlich, M; Ruther, T

2008-08-01

Memorising and processing faces is a short-term memory dependent task of utmost importance in the security domain, in which constant and high performance is a must. Especially in access or passport control-related tasks, the timely identification of performance decrements is essential, margins of error are narrow and inadequate performance may have grave consequences. However, conventional short-term memory tests frequently use abstract settings with little relevance to working situations. They may thus be unable to capture task-specific decrements. The aim of the study was to devise and validate a new test, better reflecting job specifics and employing appropriate stimuli. After 1.5 s (short) or 4.5 s (long) presentation, a set of seven portraits of faces had to be memorised for comparison with two control stimuli. Stimulus appearance followed 2 s (first item) and 8 s (second item) after set presentation. Twenty eight subjects (12 male, 16 female) were tested at seven different times of day, 3 h apart. Recognition rates were above 60% even for the least favourable condition. Recognition was significantly better in the 'long' condition (+10%) and for the first item (+18%). Recognition time showed significant differences (10%) between items. Minor effects of learning were found for response latencies only. Based on occupationally relevant metrics, the test displayed internal and external validity, consistency and suitability for further use in test/retest scenarios. In public security, especially where access to restricted areas is monitored, margins of error are narrow and operator performance must remain high and level. Appropriate schedules for personnel, based on valid test results, are required. However, task-specific data and performance tests, permitting the description of task specific decrements, are not available. Commonly used tests may be unsuitable due to undue abstraction and insufficient reference to real-world conditions. Thus, tests are required that account for task-specific conditions and neurophysiological characteristics.

Critical thinking skills in nursing students: comparison of simulation-based performance with metrics.

PubMed

Fero, Laura J; O'Donnell, John M; Zullo, Thomas G; Dabbs, Annette DeVito; Kitutu, Julius; Samosky, Joseph T; Hoffman, Leslie A

2010-10-01

This paper is a report of an examination of the relationship between metrics of critical thinking skills and performance in simulated clinical scenarios. Paper and pencil assessments are commonly used to assess critical thinking but may not reflect simulated performance. In 2007, a convenience sample of 36 nursing students participated in measurement of critical thinking skills and simulation-based performance using videotaped vignettes, high-fidelity human simulation, the California Critical Thinking Disposition Inventory and California Critical Thinking Skills Test. Simulation-based performance was rated as 'meeting' or 'not meeting' overall expectations. Test scores were categorized as strong, average, or weak. Most (75.0%) students did not meet overall performance expectations using videotaped vignettes or high-fidelity human simulation; most difficulty related to problem recognition and reporting findings to the physician. There was no difference between overall performance based on method of assessment (P = 0.277). More students met subcategory expectations for initiating nursing interventions (P ≤ 0.001) using high-fidelity human simulation. The relationship between videotaped vignette performance and critical thinking disposition or skills scores was not statistically significant, except for problem recognition and overall critical thinking skills scores (Cramer's V = 0.444, P = 0.029). There was a statistically significant relationship between overall high-fidelity human simulation performance and overall critical thinking disposition scores (Cramer's V = 0.413, P = 0.047). Students' performance reflected difficulty meeting expectations in simulated clinical scenarios. High-fidelity human simulation performance appeared to approximate scores on metrics of critical thinking best. Further research is needed to determine if simulation-based performance correlates with critical thinking skills in the clinical setting. © 2010 The Authors. Journal of Advanced Nursing © 2010 Blackwell Publishing Ltd.

Critical thinking skills in nursing students: comparison of simulation-based performance with metrics

PubMed Central

Fero, Laura J.; O’Donnell, John M.; Zullo, Thomas G.; Dabbs, Annette DeVito; Kitutu, Julius; Samosky, Joseph T.; Hoffman, Leslie A.

2018-01-01

Aim This paper is a report of an examination of the relationship between metrics of critical thinking skills and performance in simulated clinical scenarios. Background Paper and pencil assessments are commonly used to assess critical thinking but may not reflect simulated performance. Methods In 2007, a convenience sample of 36 nursing students participated in measurement of critical thinking skills and simulation-based performance using videotaped vignettes, high-fidelity human simulation, the California Critical Thinking Disposition Inventory and California Critical Thinking Skills Test. Simulation- based performance was rated as ‘meeting’ or ‘not meeting’ overall expectations. Test scores were categorized as strong, average, or weak. Results Most (75·0%) students did not meet overall performance expectations using videotaped vignettes or high-fidelity human simulation; most difficulty related to problem recognition and reporting findings to the physician. There was no difference between overall performance based on method of assessment (P = 0·277). More students met subcategory expectations for initiating nursing interventions (P ≤ 0·001) using high-fidelity human simulation. The relationship between video-taped vignette performance and critical thinking disposition or skills scores was not statistically significant, except for problem recognition and overall critical thinking skills scores (Cramer’s V = 0·444, P = 0·029). There was a statistically significant relationship between overall high-fidelity human simulation performance and overall critical thinking disposition scores (Cramer’s V = 0·413, P = 0·047). Conclusion Students’ performance reflected difficulty meeting expectations in simulated clinical scenarios. High-fidelity human simulation performance appeared to approximate scores on metrics of critical thinking best. Further research is needed to determine if simulation-based performance correlates with critical thinking skills in the clinical setting. PMID:20636471

Challenging ocular image recognition

NASA Astrophysics Data System (ADS)

Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

2011-06-01

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

Effects of compression and individual variability on face recognition performance

NASA Astrophysics Data System (ADS)

McGarry, Delia P.; Arndt, Craig M.; McCabe, Steven A.; D'Amato, Donald P.

2004-08-01

The Enhanced Border Security and Visa Entry Reform Act of 2002 requires that the Visa Waiver Program be available only to countries that have a program to issue to their nationals machine-readable passports incorporating biometric identifiers complying with applicable standards established by the International Civil Aviation Organization (ICAO). In June 2002, the New Technologies Working Group of ICAO unanimously endorsed the use of face recognition (FR) as the globally interoperable biometric for machine-assisted identity confirmation with machine-readable travel documents (MRTDs), although Member States may elect to use fingerprint and/or iris recognition as additional biometric technologies. The means and formats are still being developed through which biometric information might be stored in the constrained space of integrated circuit chips embedded within travel documents. Such information will be stored in an open, yet unalterable and very compact format, probably as digitally signed and efficiently compressed images. The objective of this research is to characterize the many factors that affect FR system performance with respect to the legislated mandates concerning FR. A photograph acquisition environment and a commercial face recognition system have been installed at Mitretek, and over 1,400 images have been collected of volunteers. The image database and FR system are being used to analyze the effects of lossy image compression, individual differences, such as eyeglasses and facial hair, and the acquisition environment on FR system performance. Images are compressed by varying ratios using JPEG2000 to determine the trade-off points between recognition accuracy and compression ratio. The various acquisition factors that contribute to differences in FR system performance among individuals are also being measured. The results of this study will be used to refine and test efficient face image interchange standards that ensure highly accurate recognition, both for automated FR systems and human inspectors. Working within the M1-Biometrics Technical Committee of the InterNational Committee for Information Technology Standards (INCITS) organization, a standard face image format will be tested and submitted to organizations such as ICAO.

RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

NASA Astrophysics Data System (ADS)

Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

2017-03-01

Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

A segmentation-free approach to Arabic and Urdu OCR

NASA Astrophysics Data System (ADS)

Sabbour, Nazly; Shafait, Faisal

2013-01-01

In this paper, we present a generic Optical Character Recognition system for Arabic script languages called Nabocr. Nabocr uses OCR approaches specific for Arabic script recognition. Performing recognition on Arabic script text is relatively more difficult than Latin text due to the nature of Arabic script, which is cursive and context sensitive. Moreover, Arabic script has different writing styles that vary in complexity. Nabocr is initially trained to recognize both Urdu Nastaleeq and Arabic Naskh fonts. However, it can be trained by users to be used for other Arabic script languages. We have evaluated our system's performance for both Urdu and Arabic. In order to evaluate Urdu recognition, we have generated a dataset of Urdu text called UPTI (Urdu Printed Text Image Database), which measures different aspects of a recognition system. The performance of our system for Urdu clean text is 91%. For Arabic clean text, the performance is 86%. Moreover, we have compared the performance of our system against Tesseract's newly released Arabic recognition, and the performance of both systems on clean images is almost the same.

Modeling Spoken Word Recognition Performance by Pediatric Cochlear Implant Users using Feature Identification

PubMed Central

Frisch, Stefan A.; Pisoni, David B.

2012-01-01

Objective Computational simulations were carried out to evaluate the appropriateness of several psycholinguistic theories of spoken word recognition for children who use cochlear implants. These models also investigate the interrelations of commonly used measures of closed-set and open-set tests of speech perception. Design A software simulation of phoneme recognition performance was developed that uses feature identification scores as input. Two simulations of lexical access were developed. In one, early phoneme decisions are used in a lexical search to find the best matching candidate. In the second, phoneme decisions are made only when lexical access occurs. Simulated phoneme and word identification performance was then applied to behavioral data from the Phonetically Balanced Kindergarten test and Lexical Neighborhood Test of open-set word recognition. Simulations of performance were evaluated for children with prelingual sensorineural hearing loss who use cochlear implants with the MPEAK or SPEAK coding strategies. Results Open-set word recognition performance can be successfully predicted using feature identification scores. In addition, we observed no qualitative differences in performance between children using MPEAK and SPEAK, suggesting that both groups of children process spoken words similarly despite differences in input. Word recognition ability was best predicted in the model in which phoneme decisions were delayed until lexical access. Conclusions Closed-set feature identification and open-set word recognition focus on different, but related, levels of language processing. Additional insight for clinical intervention may be achieved by collecting both types of data. The most successful model of performance is consistent with current psycholinguistic theories of spoken word recognition. Thus it appears that the cognitive process of spoken word recognition is fundamentally the same for pediatric cochlear implant users and children and adults with normal hearing. PMID:11132784

Estrous cycle, pregnancy, and parity enhance performance of rats in object recognition or object placement tasks

PubMed Central

Paris, Jason J; Frye, Cheryl A

2008-01-01

Ovarian hormone elevations are associated with enhanced learning/memory. During behavioral estrus or pregnancy, progestins, such as progesterone (P4) and its metabolite 5α-pregnan-3α-ol-20-one (3α,5α-THP), are elevated due, in part, to corpora luteal and placental secretion. During ‘pseudopregnancy’, the induction of corpora luteal functioning results in a hormonal milieu analogous to pregnancy, which ceases after about 12 days, due to the lack of placental formation. Multiparity is also associated with enhanced learning/memory, perhaps due to prior steroid exposure during pregnancy. Given evidence that progestins and/or parity may influence cognition, we investigated how natural alterations in the progestin milieu influence cognitive performance. In Experiment 1, virgin rats (nulliparous) or rats with two prior pregnancies (multiparous) were assessed on the object placement and recognition tasks, when in high-estrogen/P4 (behavioral estrus) or low-estrogen/P4 (diestrus) phases of the estrous cycle. In Experiment 2, primiparous or multiparous rats were tested in the object placement and recognition tasks when not pregnant, pseudopregnant, or pregnant (between gestational days (GDs) 6 and 12). In Experiment 3, pregnant primiparous or multiparous rats were assessed daily in the object placement or recognition tasks. Females in natural states associated with higher endogenous progestins (behavioral estrus, pregnancy, multiparity) outperformed rats in low progestin states (diestrus, non-pregnancy, nulliparity) on the object placement and recognition tasks. In earlier pregnancy, multiparous, compared with primiparous, rats had a lower corticosterone, but higher estrogen levels, concomitant with better object placement performance. From GD 13 until post partum, primiparous rats had higher 3α,5α-THP levels and improved object placement performance compared with multiparous rats. PMID:18390689

Application of Classification Models to Pharyngeal High-Resolution Manometry

ERIC Educational Resources Information Center

Mielens, Jason D.; Hoffman, Matthew R.; Ciucci, Michelle R.; McCulloch, Timothy M.; Jiang, Jack J.

2012-01-01

Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were…

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barstow, Del R; Patlolla, Dilip Reddy; Mann, Christopher J

Abstract The data captured by existing standoff biometric systems typically has lower biometric recognition performance than their close range counterparts due to imaging challenges, pose challenges, and other factors. To assist in overcoming these limitations systems typically perform in a multi-modal capacity such as Honeywell s Combined Face and Iris (CFAIRS) [21] system. While this improves the systems performance, standoff systems have yet to be proven as accurate as their close range equivalents. We will present a standoff system capable of operating up to 7 meters in range. Unlike many systems such as the CFAIRS our system captures high qualitymore » 12 MP video allowing for a multi-sample as well as multi-modal comparison. We found that for standoff systems multi-sample improved performance more than multi-modal. For a small test group of 50 subjects we were able to achieve 100% rank one recognition performance with our system.« less

A high-throughput screening approach to discovering good forms of biologically inspired visual representation.

PubMed

Pinto, Nicolas; Doukhan, David; DiCarlo, James J; Cox, David D

2009-11-01

While many models of biological object recognition share a common set of "broad-stroke" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct "parts" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

PubMed Central

Pinto, Nicolas; Doukhan, David; DiCarlo, James J.; Cox, David D.

2009-01-01

While many models of biological object recognition share a common set of “broad-stroke” properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model—e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct “parts” have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision. PMID:19956750

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

PubMed Central

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-01-01

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions. PMID:28208684

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

PubMed

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-02-12

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Human activities recognition by head movement using partial recurrent neural network

NASA Astrophysics Data System (ADS)

Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

2003-06-01

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

Neurobiological mechanisms associated with facial affect recognition deficits after traumatic brain injury.

PubMed

Neumann, Dawn; McDonald, Brenna C; West, John; Keiski, Michelle A; Wang, Yang

2016-06-01

The neurobiological mechanisms that underlie facial affect recognition deficits after traumatic brain injury (TBI) have not yet been identified. Using functional magnetic resonance imaging (fMRI), study aims were to 1) determine if there are differences in brain activation during facial affect processing in people with TBI who have facial affect recognition impairments (TBI-I) relative to people with TBI and healthy controls who do not have facial affect recognition impairments (TBI-N and HC, respectively); and 2) identify relationships between neural activity and facial affect recognition performance. A facial affect recognition screening task performed outside the scanner was used to determine group classification; TBI patients who performed greater than one standard deviation below normal performance scores were classified as TBI-I, while TBI patients with normal scores were classified as TBI-N. An fMRI facial recognition paradigm was then performed within the 3T environment. Results from 35 participants are reported (TBI-I = 11, TBI-N = 12, and HC = 12). For the fMRI task, TBI-I and TBI-N groups scored significantly lower than the HC group. Blood oxygenation level-dependent (BOLD) signals for facial affect recognition compared to a baseline condition of viewing a scrambled face, revealed lower neural activation in the right fusiform gyrus (FG) in the TBI-I group than the HC group. Right fusiform gyrus activity correlated with accuracy on the facial affect recognition tasks (both within and outside the scanner). Decreased FG activity suggests facial affect recognition deficits after TBI may be the result of impaired holistic face processing. Future directions and clinical implications are discussed.

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

NASA Astrophysics Data System (ADS)

Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

2016-10-01

Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

Transfer Learning for Activity Recognition: A Survey

PubMed Central

Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326

Test battery for measuring the perception and recognition of facial expressions of emotion

PubMed Central

Wilhelm, Oliver; Hildebrandt, Andrea; Manske, Karsten; Schacht, Annekathrin; Sommer, Werner

2014-01-01

Despite the importance of perceiving and recognizing facial expressions in everyday life, there is no comprehensive test battery for the multivariate assessment of these abilities. As a first step toward such a compilation, we present 16 tasks that measure the perception and recognition of facial emotion expressions, and data illustrating each task's difficulty and reliability. The scoring of these tasks focuses on either the speed or accuracy of performance. A sample of 269 healthy young adults completed all tasks. In general, accuracy and reaction time measures for emotion-general scores showed acceptable and high estimates of internal consistency and factor reliability. Emotion-specific scores yielded lower reliabilities, yet high enough to encourage further studies with such measures. Analyses of task difficulty revealed that all tasks are suitable for measuring emotion perception and emotion recognition related abilities in normal populations. PMID:24860528

Incidental Memory Encoding Assessed with Signal Detection Theory and Functional Magnetic Resonance Imaging (fMRI).

PubMed

Clemens, Benjamin; Regenbogen, Christina; Koch, Kathrin; Backes, Volker; Romanczuk-Seiferth, Nina; Pauly, Katharina; Shah, N Jon; Schneider, Frank; Habel, Ute; Kellermann, Thilo

2015-01-01

In functional magnetic resonance imaging (fMRI) studies that apply a "subsequent memory" approach, successful encoding is indicated by increased fMRI activity during the encoding phase for hits vs. misses, in areas underlying memory encoding such as the hippocampal formation. Signal-detection theory (SDT) can be used to analyze memory-related fMRI activity as a function of the participant's memory trace strength (d(')). The goal of the present study was to use SDT to examine the relationship between fMRI activity during incidental encoding and participants' recognition performance. To implement a new approach, post-experimental group assignment into High- or Low Performers (HP or LP) was based on 29 healthy participants' recognition performance, assessed with SDT. The analyses focused on the interaction between the factors group (HP vs. LP) and recognition performance (hits vs. misses). A whole-brain analysis revealed increased activation for HP vs. LP during incidental encoding for remembered vs. forgotten items (hits > misses) in the insula/temporo-parietal junction (TPJ) and the fusiform gyrus (FFG). Parameter estimates in these regions exhibited a significant positive correlation with d('). As these brain regions are highly relevant for salience detection (insula), stimulus-driven attention (TPJ), and content-specific processing of mnemonic stimuli (FFG), we suggest that HPs' elevated memory performance was associated with enhanced attentional and content-specific sensory processing during the encoding phase. We provide first correlative evidence that encoding-related activity in content-specific sensory areas and content-independent attention and salience detection areas influences memory performance in a task with incidental encoding of facial stimuli. Based on our findings, we discuss whether the aforementioned group differences in brain activity during incidental encoding might constitute the basis of general differences in memory performance between HP and LP.

Visual abilities are important for auditory-only speech recognition: evidence from autism spectrum disorder.

PubMed

Schelinski, Stefanie; Riedel, Philipp; von Kriegstein, Katharina

2014-12-01

In auditory-only conditions, for example when we listen to someone on the phone, it is essential to fast and accurately recognize what is said (speech recognition). Previous studies have shown that speech recognition performance in auditory-only conditions is better if the speaker is known not only by voice, but also by face. Here, we tested the hypothesis that such an improvement in auditory-only speech recognition depends on the ability to lip-read. To test this we recruited a group of adults with autism spectrum disorder (ASD), a condition associated with difficulties in lip-reading, and typically developed controls. All participants were trained to identify six speakers by name and voice. Three speakers were learned by a video showing their face and three others were learned in a matched control condition without face. After training, participants performed an auditory-only speech recognition test that consisted of sentences spoken by the trained speakers. As a control condition, the test also included speaker identity recognition on the same auditory material. The results showed that, in the control group, performance in speech recognition was improved for speakers known by face in comparison to speakers learned in the matched control condition without face. The ASD group lacked such a performance benefit. For the ASD group auditory-only speech recognition was even worse for speakers known by face compared to speakers not known by face. In speaker identity recognition, the ASD group performed worse than the control group independent of whether the speakers were learned with or without face. Two additional visual experiments showed that the ASD group performed worse in lip-reading whereas face identity recognition was within the normal range. The findings support the view that auditory-only communication involves specific visual mechanisms. Further, they indicate that in ASD, speaker-specific dynamic visual information is not available to optimize auditory-only speech recognition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

Tactile agnosia. Underlying impairment and implications for normal tactile object recognition.

PubMed

Reed, C L; Caselli, R J; Farah, M J

1996-06-01

In a series of experimental investigations of a subject with a unilateral impairment of tactile object recognition without impaired tactile sensation, several issues were addressed. First, is tactile agnosia secondary to a general impairment of spatial cognition? On tests of spatial ability, including those directed at the same spatial integration process assumed to be taxed by tactile object recognition, the subject performed well, implying a more specific impairment of high level, modality specific tactile perception. Secondly, within the realm of high level tactile perception, is there a distinction between the ability to derive shape ('what') and spatial ('where') information? Our testing showed an impairment confined to shape perception. Thirdly, what aspects of shape perception are impaired in tactile agnosia? Our results indicate that despite accurate encoding of metric length and normal manual exploration strategies, the ability tactually to perceive objects with the impaired hand, deteriorated as the complexity of shape increased. In addition, asymmetrical performance was not found for other body surfaces (e.g. her feet). Our results suggest that tactile shape perception can be disrupted independent of general spatial ability, tactile spatial ability, manual shape exploration, or even the precise perception of metric length in the tactile modality.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

Temporal voice areas exist in autism spectrum disorder but are dysfunctional for voice identity recognition

PubMed Central

Borowiak, Kamila; von Kriegstein, Katharina

2016-01-01

The ability to recognise the identity of others is a key requirement for successful communication. Brain regions that respond selectively to voices exist in humans from early infancy on. Currently, it is unclear whether dysfunction of these voice-sensitive regions can explain voice identity recognition impairments. Here, we used two independent functional magnetic resonance imaging studies to investigate voice processing in a population that has been reported to have no voice-sensitive regions: autism spectrum disorder (ASD). Our results refute the earlier report that individuals with ASD have no responses in voice-sensitive regions: Passive listening to vocal, compared to non-vocal, sounds elicited typical responses in voice-sensitive regions in the high-functioning ASD group and controls. In contrast, the ASD group had a dysfunction in voice-sensitive regions during voice identity but not speech recognition in the right posterior superior temporal sulcus/gyrus (STS/STG)—a region implicated in processing complex spectrotemporal voice features and unfamiliar voices. The right anterior STS/STG correlated with voice identity recognition performance in controls but not in the ASD group. The findings suggest that right STS/STG dysfunction is critical for explaining voice recognition impairments in high-functioning ASD and show that ASD is not characterised by a general lack of voice-sensitive responses. PMID:27369067

Neural basis for recognition confidence in younger and older adults.

PubMed

Chua, Elizabeth F; Schacter, Daniel L; Sperling, Reisa A

2009-03-01

Although several studies have examined the neural basis for age-related changes in objective memory performance, less is known about how the process of memory monitoring changes with aging. The authors used functional magnetic resonance imaging to examine retrospective confidence in memory performance in aging. During low confidence, both younger and older adults showed behavioral evidence that they were guessing during recognition and that they were aware they were guessing when making confidence judgments. Similarly, both younger and older adults showed increased neural activity during low- compared to high-confidence responses in the lateral prefrontal cortex, anterior cingulate cortex, and left intraparietal sulcus. In contrast, older adults showed more high-confidence errors than younger adults. Younger adults showed greater activity for high compared to low confidence in medial temporal lobe structures, but older adults did not show this pattern. Taken together, these findings may suggest that impairments in the confidence-accuracy relationship for memory in older adults, which are often driven by high-confidence errors, may be primarily related to altered neural signals associated with greater activity for high-confidence responses.

Neural basis for recognition confidence in younger and older adults

PubMed Central

Chua, Elizabeth F.; Schacter, Daniel L.; Sperling, Reisa A.

2008-01-01

Although several studies have examined the neural basis for age-related changes in objective memory performance, less is known about how the process of memory monitoring changes with aging. We used fMRI to examine retrospective confidence in memory performance in aging. During low confidence, both younger and older adults showed behavioral evidence that they were guessing during recognition, and that they were aware they were guessing when making confidence judgments. Similarly, both younger and older adults showed increased neural activity during low compared to high confidence responses in lateral prefrontal cortex, anterior cingulate cortex, and left intraparietal sulcus. In contrast, older adults showed more high confidence errors than younger adults. Younger adults showed greater activity for high compared to low confidence in medial temporal lobe structures, but older adults did not show this pattern. Taken together, these findings may suggest that impairments in the confidence-accuracy relationship for memory in older adults, which are often driven by high confidence errors, may be primarily related to altered neural signals associated with greater activity for high confidence responses. PMID:19290745

Lexical and age effects on word recognition in noise in normal-hearing children.

PubMed

Ren, Cuncun; Liu, Sha; Liu, Haihong; Kong, Ying; Liu, Xin; Li, Shujing

2015-12-01

The purposes of the present study were (1) to examine the lexical and age effects on word recognition of normal-hearing (NH) children in noise, and (2) to compare the word-recognition performance in noise to that in quiet listening conditions. Participants were 213 NH children (age ranged between 3 and 6 years old). Eighty-nine and 124 of the participants were tested in noise and quiet listening conditions, respectively. The Standard-Chinese Lexical Neighborhood Test, which contains lists of words in four lexical categories (i.e., dissyllablic easy (DE), dissyllablic hard (DH), monosyllable easy (ME), and monosyllable hard (MH)) was used to evaluate the Mandarin Chinese word recognition in speech spectrum-shaped noise (SSN) with a signal-to-noise ratio (SNR) of 0dB. A two-way repeated-measures analysis of variance was conducted to examine the lexical effects with syllable length and difficulty level as the main factors on word recognition in the quiet and noise listening conditions. The effects of age on word-recognition performance were examined using a regression model. The word-recognition performance in noise was significantly poorer than that in quiet and the individual variations in performance in noise were much greater than those in quiet. Word recognition scores showed that the lexical effects were significant in the SSN. Children scored higher with dissyllabic words than with monosyllabic words; "easy" words scored higher than "hard" words in the noise condition. The scores of the NH children in the SSN (SNR=0dB) for the DE, DH, ME, and MH words were 85.4, 65.9, 71.7, and 46.2% correct, respectively. The word-recognition performance also increased with age in each lexical category for the NH children tested in noise. Both age and lexical characteristics of words had significant influences on the performance of Mandarin-Chinese word recognition in noise. The lexical effects were more obvious under noise listening conditions than in quiet. The word-recognition performance in noise increased with age in NH children of 3-6 years old and had not reached plateau at 6 years of age in the NH children. Copyright © 2015. Published by Elsevier Ireland Ltd.

Effects of level of processing but not of task enactment on recognition memory in a case of developmental amnesia.

PubMed

Gardiner, John M; Brandt, Karen R; Vargha-Khadem, Faraneh; Baddeley, Alan; Mishkin, Mortimer

2006-09-01

We report the performance in four recognition memory experiments of Jon, a young adult with early-onset developmental amnesia whose episodic memory is gravely impaired in tests of recall, but seems relatively preserved in tests of recognition, and who has developed normal levels of performance in tests of intelligence and general knowledge. Jon's recognition performance was enhanced by deeper levels of processing in comparing a more meaningful study task with a less meaningful one, but not by task enactment in comparing performance of an action with reading an action phrase. Both of these variables normally enhance episodic remembering, which Jon claimed to experience. But Jon was unable to support that claim by recollecting what it was that he remembered. Taken altogether, the findings strongly imply that Jon's recognition performance entailed little genuine episodic remembering and that the levels-of-processing effects in Jon reflected semantic, not episodic, memory.

Performing speech recognition research with hypercard

NASA Technical Reports Server (NTRS)

Shepherd, Chip

1993-01-01

The purpose of this paper is to describe a HyperCard-based system for performing speech recognition research and to instruct Human Factors professionals on how to use the system to obtain detailed data about the user interface of a prototype speech recognition application.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Cultural differences in self-recognition: the early development of autonomous and related selves?

PubMed

Ross, Josephine; Yilmaz, Mandy; Dale, Rachel; Cassidy, Rose; Yildirim, Iraz; Suzanne Zeedyk, M

2017-05-01

Fifteen- to 18-month-old infants from three nationalities were observed interacting with their mothers and during two self-recognition tasks. Scottish interactions were characterized by distal contact, Zambian interactions by proximal contact, and Turkish interactions by a mixture of contact strategies. These culturally distinct experiences may scaffold different perspectives on self. In support, Scottish infants performed best in a task requiring recognition of the self in an individualistic context (mirror self-recognition), whereas Zambian infants performed best in a task requiring recognition of the self in a less individualistic context (body-as-obstacle task). Turkish infants performed similarly to Zambian infants on the body-as-obstacle task, but outperformed Zambians on the mirror self-recognition task. Verbal contact (a distal strategy) was positively related to mirror self-recognition and negatively related to passing the body-as-obstacle task. Directive action and speech (proximal strategies) were negatively related to mirror self-recognition. Self-awareness performance was best predicted by cultural context; autonomous settings predicted success in mirror self-recognition, and related settings predicted success in the body-as-obstacle task. These novel data substantiate the idea that cultural factors may play a role in the early expression of self-awareness. More broadly, the results highlight the importance of moving beyond the mark test, and designing culturally sensitive tests of self-awareness. © 2016 John Wiley & Sons Ltd.

Effects of noise on speech recognition: Challenges for communication by service members.

PubMed

Le Prell, Colleen G; Clavier, Odile H

2017-06-01

Speech communication often takes place in noisy environments; this is an urgent issue for military personnel who must communicate in high-noise environments. The effects of noise on speech recognition vary significantly according to the sources of noise, the number and types of talkers, and the listener's hearing ability. In this review, speech communication is first described as it relates to current standards of hearing assessment for military and civilian populations. The next section categorizes types of noise (also called maskers) according to their temporal characteristics (steady or fluctuating) and perceptive effects (energetic or informational masking). Next, speech recognition difficulties experienced by listeners with hearing loss and by older listeners are summarized, and questions on the possible causes of speech-in-noise difficulty are discussed, including recent suggestions of "hidden hearing loss". The final section describes tests used by military and civilian researchers, audiologists, and hearing technicians to assess performance of an individual in recognizing speech in background noise, as well as metrics that predict performance based on a listener and background noise profile. This article provides readers with an overview of the challenges associated with speech communication in noisy backgrounds, as well as its assessment and potential impact on functional performance, and provides guidance for important new research directions relevant not only to military personnel, but also to employees who work in high noise environments. Copyright © 2016 Elsevier B.V. All rights reserved.

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Examining the Relationships among Item Recognition, Source Recognition, and Recall from an Individual Differences Perspective

ERIC Educational Resources Information Center

Unsworth, Nash; Brewer, Gene A.

2009-01-01

The authors of the current study examined the relationships among item-recognition, source-recognition, free recall, and other memory and cognitive ability tasks via an individual differences analysis. Two independent sources of variance contributed to item-recognition and source-recognition performance, and these two constructs related…

Physics career intentions: The effect of physics identity, math identity, and gender

NASA Astrophysics Data System (ADS)

Lock, Robynne M.; Hazari, Zahra; Potvin, Geoff

2013-01-01

Although nearly half of high school physics students are female, only 21% of physics bachelor's degrees are earned by women. Using data from a national survey of college students in introductory English courses (on science-related experiences, particularly in high school), we examine the influence of students' physics and math identities on their choice to pursue a physics career. Males have higher math and physics identities than females in all three dimensions of our identity framework. These dimensions include: performance/competence (perceptions of ability to perform/understand), recognition (perception of recognition by others), and interest (desire to learn more). A regression model predicting students' intentions to pursue physics careers shows, as expected, that males are significantly more likely to choose physics than females. Surprisingly, however, when physics and math identity are included in the model, females are shown to be equally likely to choose physics careers as compared to males.

Poly(ionic liquid) based chemosensors for detection of basic amino acids in aqueous medium

NASA Astrophysics Data System (ADS)

Li, Xinjuan; Wang, Kai; Ma, Nana; Jia, Xianbin

2017-09-01

Naked-eye detection of amino acids in water is of great significance in the field of bio-analytical applications. Herein, polymerized ionic liquids (PILs) with controlled chain length structures were synthesized via reversible addition-fragmentation chain-transfer (RAFT) polymerization and post-quaternization approach. The amino acids recognition performance of PILs with different alkyl chain lengths and molecular weights was evaluated by naked-eye color change and ultraviolet-visible (UV-vis) spectral studies. These PILs were successfully used for highly sensitive and selective detection of Arg, Lys and His in water. The recognition performance was improved effectively with increased molecular weight of PILs. The biosensitivity of the PILs in water was strongly dependent on their aggregation effect and polarization effect. Highly sensitive and selective detection of amino acids was successfully accomplished by introducing positively charged pyridinium moieties and controlled RAFT radical polymerization.

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

PubMed Central

Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

2017-01-01

RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

Lexical tone recognition in noise in normal-hearing children and prelingually deafened children with cochlear implants

PubMed Central

Mao, Yitao; Xu, Li

2016-01-01

Objective The purpose of the present study was to investigate Mandarin tone recognition in background noise in children with cochlear implants (CIs), and to examine the potential factors contributing to their performance. Design Tone recognition was tested using a two-alternative forced-choice paradigm in various signal-to-noise ratio (SNR) conditions (i.e., quiet, +12, +6, 0, and −6 dB). Linear correlation analysis was performed to examine possible relationships between the tone-recognition performance of the CI children and the demographic factors. Study sample Sixty-six prelingually deafened children with CIs and 52 normal-hearing (NH) children as controls participated in the study. Results Children with CIs showed overall poorer tone-recognition performance and were more susceptible to noise than their NH peers. Tone confusions between Mandarin tone 2 and tone 3 were most prominent in both CI and NH children except for in the poorest SNR conditions. Age at implantation was significantly correlated with tone-recognition performance of the CI children in noise. Conclusions There is a marked deficit in tone recognition in prelingually deafened children with CIs, particularly in noise listening conditions. While factors that contribute to the large individual differences are still elusive, early implantation could be beneficial to tone development in pediatric CI users. PMID:27564095

Lexical tone recognition in noise in normal-hearing children and prelingually deafened children with cochlear implants.

PubMed

Mao, Yitao; Xu, Li

2017-01-01

The purpose of the present study was to investigate Mandarin tone recognition in background noise in children with cochlear implants (CIs), and to examine the potential factors contributing to their performance. Tone recognition was tested using a two-alternative forced-choice paradigm in various signal-to-noise ratio (SNR) conditions (i.e. quiet, +12, +6, 0, and -6 dB). Linear correlation analysis was performed to examine possible relationships between the tone-recognition performance of the CI children and the demographic factors. Sixty-six prelingually deafened children with CIs and 52 normal-hearing (NH) children as controls participated in the study. Children with CIs showed an overall poorer tone-recognition performance and were more susceptible to noise than their NH peers. Tone confusions between Mandarin tone 2 and tone 3 were most prominent in both CI and NH children except for in the poorest SNR conditions. Age at implantation was significantly correlated with tone-recognition performance of the CI children in noise. There is a marked deficit in tone recognition in prelingually deafened children with CIs, particularly in noise listening conditions. While factors that contribute to the large individual differences are still elusive, early implantation could be beneficial to tone development in pediatric CI users.

Effects of Minority Status on Facial Recognition and Naming Performance.

ERIC Educational Resources Information Center

Roberts, Richard J.; Hamsher, Kerry

1984-01-01

Examined the differential effects of minority status in Blacks (N=94) on a facial recognition test and a naming test. Results showed that performance on the facial recognition test was relatively free of racial bias, but this was not the case for visual naming. (LLL)

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition

PubMed Central

2017-01-01

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user’s location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively. PMID:28817094

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition.

PubMed

Choi, Hyo-Rim; Kim, TaeYong

2017-08-17

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user's location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

Speaker normalization for chinese vowel recognition in cochlear implants.

PubMed

Luo, Xin; Fu, Qian-Jie

2005-07-01

Because of the limited spectra-temporal resolution associated with cochlear implants, implant patients often have greater difficulty with multitalker speech recognition. The present study investigated whether multitalker speech recognition can be improved by applying speaker normalization techniques to cochlear implant speech processing. Multitalker Chinese vowel recognition was tested with normal-hearing Chinese-speaking subjects listening to a 4-channel cochlear implant simulation, with and without speaker normalization. For each subject, speaker normalization was referenced to the speaker that produced the best recognition performance under conditions without speaker normalization. To match the remaining speakers to this "optimal" output pattern, the overall frequency range of the analysis filter bank was adjusted for each speaker according to the ratio of the mean third formant frequency values between the specific speaker and the reference speaker. Results showed that speaker normalization provided a small but significant improvement in subjects' overall recognition performance. After speaker normalization, subjects' patterns of recognition performance across speakers changed, demonstrating the potential for speaker-dependent effects with the proposed normalization technique.

The association between imitation recognition and socio-communicative competencies in chimpanzees (Pan troglodytes).

PubMed

Pope, Sarah M; Russell, Jamie L; Hopkins, William D

2015-01-01

Imitation recognition provides a viable platform from which advanced social cognitive skills may develop. Despite evidence that non-human primates are capable of imitation recognition, how this ability is related to social cognitive skills is unknown. In this study, we compared imitation recognition performance, as indicated by the production of testing behaviors, with performance on a series of tasks that assess social and physical cognition in 49 chimpanzees. In the initial analyses, we found that males were more responsive than females to being imitated and engaged in significantly greater behavior repetitions and testing sequences. We also found that subjects who consistently recognized being imitated performed better on social but not physical cognitive tasks, as measured by the Primate Cognitive Test Battery. These findings suggest that the neural constructs underlying imitation recognition are likely associated with or among those underlying more general socio-communicative abilities in chimpanzees. Implications regarding how imitation recognition may facilitate other social cognitive processes, such as mirror self-recognition, are discussed.

The association between imitation recognition and socio-communicative competencies in chimpanzees (Pan troglodytes)

PubMed Central

Pope, Sarah M.; Russell, Jamie L.; Hopkins, William D.

2015-01-01

Imitation recognition provides a viable platform from which advanced social cognitive skills may develop. Despite evidence that non-human primates are capable of imitation recognition, how this ability is related to social cognitive skills is unknown. In this study, we compared imitation recognition performance, as indicated by the production of testing behaviors, with performance on a series of tasks that assess social and physical cognition in 49 chimpanzees. In the initial analyses, we found that males were more responsive than females to being imitated and engaged in significantly greater behavior repetitions and testing sequences. We also found that subjects who consistently recognized being imitated performed better on social but not physical cognitive tasks, as measured by the Primate Cognitive Test Battery. These findings suggest that the neural constructs underlying imitation recognition are likely associated with or among those underlying more general socio-communicative abilities in chimpanzees. Implications regarding how imitation recognition may facilitate other social cognitive processes, such as mirror self-recognition, are discussed. PMID:25767454

New Trends in Impedimetric Biosensors for the Detection of Foodborne Pathogenic Bacteria

PubMed Central

Wang, Yixian; Ye, Zunzhong; Ying, Yibin

2012-01-01

The development of a rapid, sensitive, specific method for the foodborne pathogenic bacteria detection is of great importance to ensure food safety and security. In recent years impedimetric biosensors which integrate biological recognition technology and impedance have gained widespread application in the field of bacteria detection. This paper presents an overview on the progress and application of impedimetric biosensors for detection of foodborne pathogenic bacteria, particularly the new trends in the past few years, including the new specific bio-recognition elements such as bacteriophage and lectin, the use of nanomaterials and microfluidics techniques. The applications of these new materials or techniques have provided unprecedented opportunities for the development of high-performance impedance bacteria biosensors. The significant developments of impedimetric biosensors for bacteria detection in the last five years have been reviewed according to the classification of with or without specific bio-recognition element. In addition, some microfluidics systems, which were used in the construction of impedimetric biosensors to improve analytical performance, are introduced in this review. PMID:22737018

Face recognition in age related macular degeneration: perceived disability, measured disability, and performance with a bioptic device.

PubMed

Tejeria, L; Harper, R A; Artes, P H; Dickinson, C M

2002-09-01

(1) To explore the relation between performance on tasks of familiar face recognition (FFR) and face expression difference discrimination (FED) with both perceived disability in face recognition and clinical measures of visual function in subjects with age related macular degeneration (AMD). (2) To quantify the gain in performance for face recognition tasks when subjects use a bioptic telescopic low vision device. 30 subjects with AMD (age range 66-90 years; visual acuity 0.4-1.4 logMAR) were recruited for the study. Perceived (self rated) disability in face recognition was assessed by an eight item questionnaire covering a range of issues relating to face recognition. Visual functions measured were distance visual acuity (ETDRS logMAR charts), continuous text reading acuity (MNRead charts), contrast sensitivity (Pelli-Robson chart), and colour vision (large panel D-15). In the FFR task, images of famous people had to be identified. FED was assessed by a forced choice test where subjects had to decide which one of four images showed a different facial expression. These tasks were repeated with subjects using a bioptic device. Overall perceived disability in face recognition did not correlate with performance on either task, although a specific item on difficulty recognising familiar faces did correlate with FFR (r = 0.49, p<0.05). FFR performance was most closely related to distance acuity (r = -0.69, p<0.001), while FED performance was most closely related to continuous text reading acuity (r = -0.79, p<0.001). In multiple regression, neither contrast sensitivity nor colour vision significantly increased the explained variance. When using a bioptic telescope, FFR performance improved in 86% of subjects (median gain = 49%; p<0.001), while FED performance increased in 79% of subjects (median gain = 50%; p<0.01). Distance and reading visual acuity are closely associated with measured task performance in FFR and FED. A bioptic low vision device can offer a significant improvement in performance for face recognition tasks, and may be useful in reducing the handicap associated with this disability. There is, however, little evidence for a correlation between self rated difficulty in face recognition and measured performance for either task. Further work is needed to explore the complex relation between the perception of disability and measured performance.

Face recognition in age related macular degeneration: perceived disability, measured disability, and performance with a bioptic device

PubMed Central

Tejeria, L; Harper, R A; Artes, P H; Dickinson, C M

2002-01-01

Aims: (1) To explore the relation between performance on tasks of familiar face recognition (FFR) and face expression difference discrimination (FED) with both perceived disability in face recognition and clinical measures of visual function in subjects with age related macular degeneration (AMD). (2) To quantify the gain in performance for face recognition tasks when subjects use a bioptic telescopic low vision device. Methods: 30 subjects with AMD (age range 66–90 years; visual acuity 0.4–1.4 logMAR) were recruited for the study. Perceived (self rated) disability in face recognition was assessed by an eight item questionnaire covering a range of issues relating to face recognition. Visual functions measured were distance visual acuity (ETDRS logMAR charts), continuous text reading acuity (MNRead charts), contrast sensitivity (Pelli-Robson chart), and colour vision (large panel D-15). In the FFR task, images of famous people had to be identified. FED was assessed by a forced choice test where subjects had to decide which one of four images showed a different facial expression. These tasks were repeated with subjects using a bioptic device. Results: Overall perceived disability in face recognition did not correlate with performance on either task, although a specific item on difficulty recognising familiar faces did correlate with FFR (r = 0.49, p<0.05). FFR performance was most closely related to distance acuity (r = −0.69, p<0.001), while FED performance was most closely related to continuous text reading acuity (r = −0.79, p<0.001). In multiple regression, neither contrast sensitivity nor colour vision significantly increased the explained variance. When using a bioptic telescope, FFR performance improved in 86% of subjects (median gain = 49%; p<0.001), while FED performance increased in 79% of subjects (median gain = 50%; p<0.01). Conclusion: Distance and reading visual acuity are closely associated with measured task performance in FFR and FED. A bioptic low vision device can offer a significant improvement in performance for face recognition tasks, and may be useful in reducing the handicap associated with this disability. There is, however, little evidence for a correlation between self rated difficulty in face recognition and measured performance for either task. Further work is needed to explore the complex relation between the perception of disability and measured performance. PMID:12185131

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

PubMed Central

Geng, Yanjuan; Wei, Yue

2017-01-01

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees. PMID:28523276

Atypical development of configural face recognition in children with autism, Down syndrome and Williams syndrome.

PubMed

Dimitriou, D; Leonard, H C; Karmiloff-Smith, A; Johnson, M H; Thomas, M S C

2015-05-01

Configural processing in face recognition is a sensitivity to the spacing between facial features. It has been argued both that its presence represents a high level of expertise in face recognition, and also that it is a developmentally vulnerable process. We report a cross-syndrome investigation of the development of configural face recognition in school-aged children with autism, Down syndrome and Williams syndrome compared with a typically developing comparison group. Cross-sectional trajectory analyses were used to compare configural and featural face recognition utilising the 'Jane faces' task. Trajectories were constructed linking featural and configural performance either to chronological age or to different measures of mental age (receptive vocabulary, visuospatial construction), as well as the Benton face recognition task. An emergent inversion effect across age for detecting configural but not featural changes in faces was established as the marker of typical development. Children from clinical groups displayed atypical profiles that differed across all groups. We discuss the implications for the nature of face processing within the respective developmental disorders, and how the cross-sectional syndrome comparison informs the constraints that shape the typical development of face recognition. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Facial recognition in children after perinatal stroke.

PubMed

Ballantyne, A O; Trauner, D A

1999-04-01

To examine the effects of prenatal or perinatal stroke on the facial recognition skills of children and young adults. It was hypothesized that the nature and extent of facial recognition deficits seen in patients with early-onset lesions would be different from that seen in adults with later-onset neurologic impairment. Numerous studies with normal and neurologically impaired adults have found a right-hemisphere superiority for facial recognition. In contrast, little is known about facial recognition in children after early focal brain damage. Forty subjects had single, unilateral brain lesions from pre- or perinatal strokes (20 had left-hemisphere damage, and 20 had right-hemisphere damage), and 40 subjects were controls who were individually matched to the lesion subjects on the basis of age, sex, and socioeconomic status. Each subject was given the Short-Form of Benton's Test of Facial Recognition. Data were analyzed using the Wilcoxon matched-pairs signed-rank test and multiple regression. The lesion subjects performed significantly more poorly than did matched controls. There was no clear-cut lateralization effect, with the left-hemisphere group performing significantly more poorly than matched controls and the right-hemisphere group showing a trend toward poorer performance. Parietal lobe involvement, regardless of lesion side, adversely affected facial recognition performance in the lesion group. Results could not be accounted for by IQ differences between lesion and control groups, nor was lesion severity systematically related to facial recognition performance. Pre- or perinatal unilateral brain damage results in a subtle disturbance in facial recognition ability, independent of the side of the lesion. Parietal lobe involvement, in particular, has an adverse effect on facial recognition skills. These findings suggest that the parietal lobes may be involved in the acquisition of facial recognition ability from a very early point in brain development, but that there is sufficient potential to reorganize or compensate such that the residual deficits, though significant, are subtle.

Infrared and visible fusion face recognition based on NSCT domain

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-01-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

That's my teacher! Children's ability to recognize personally familiar and unfamiliar faces improves with age.

PubMed

Laurence, Sarah; Mondloch, Catherine J

2016-03-01

Most previous research on the development of face recognition has focused on recognition of highly controlled images. One of the biggest challenges of face recognition is to identify an individual across images that capture natural variability in appearance. We created a child-friendly version of Jenkins, White, Van Montford, and Burton's sorting task (Cognition, 2011, Vol. 121, pp. 313-323) to investigate children's recognition of personally familiar and unfamiliar faces. Children between 4 and 12years of age were presented with a familiar/unfamiliar teacher's house and a pile of face photographs (nine pictures each of the teacher and another identity). Each child was asked to put all the pictures of the teacher inside the house while keeping the other identity out. Children over 6years of age showed adult-like familiar face recognition. Unfamiliar face recognition improved across the entire age range, with considerable variability in children's performance. These findings suggest that children's ability to tolerate within-person variability improves with age and support a face-space framework in which faces are represented as regions, the size of which increases with age. Copyright © 2015 Elsevier Inc. All rights reserved.

Concept recognition for extracting protein interaction relations from biomedical text

PubMed Central

Baumgartner, William A; Lu, Zhiyong; Johnson, Helen L; Caporaso, J Gregory; Paquette, Jesse; Lindemann, Anna; White, Elizabeth K; Medvedeva, Olga; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-01

Background: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. Results: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. Conclusion: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet . PMID:18834500

Conversion of short-term to long-term memory in the novel object recognition paradigm

PubMed Central

Moore, Shannon J.; Deshpande, Kaivalya; Stinnett, Gwen S.; Seasholtz, Audrey F.; Murphy, Geoffrey G.

2013-01-01

It is well-known that stress can significantly impact learning; however, whether this effect facilitates or impairs the resultant memory depends on the characteristics of the stressor. Investigation of these dynamics can be confounded by the role of the stressor in motivating performance in a task. Positing a cohesive model of the effect of stress on learning and memory necessitates elucidating the consequences of stressful stimuli independently from task-specific functions. Therefore, the goal of this study was to examine the effect of manipulating a task-independent stressor (elevated light level) on short-term and long-term memory in the novel object recognition paradigm. Short-term memory was elicited in both low light and high light conditions, but long-term memory specifically required high light conditions during the acquisition phase (familiarization trial) and was independent of the light level during retrieval (test trial). Additionally, long-term memory appeared to be independent of stress-mediated glucocorticoid release, as both low and high light produced similar levels of plasma corticosterone, which further did not correlate with subsequent memory performance. Finally, both short-term and long-term memory showed no savings between repeated experiments suggesting that this novel object recognition paradigm may be useful for longitudinal studies, particularly when investigating treatments to stabilize or enhance weak memories in neurodegenerative diseases or during age-related cognitive decline. PMID:23835143

Conversion of short-term to long-term memory in the novel object recognition paradigm.

PubMed

Moore, Shannon J; Deshpande, Kaivalya; Stinnett, Gwen S; Seasholtz, Audrey F; Murphy, Geoffrey G

2013-10-01

It is well-known that stress can significantly impact learning; however, whether this effect facilitates or impairs the resultant memory depends on the characteristics of the stressor. Investigation of these dynamics can be confounded by the role of the stressor in motivating performance in a task. Positing a cohesive model of the effect of stress on learning and memory necessitates elucidating the consequences of stressful stimuli independently from task-specific functions. Therefore, the goal of this study was to examine the effect of manipulating a task-independent stressor (elevated light level) on short-term and long-term memory in the novel object recognition paradigm. Short-term memory was elicited in both low light and high light conditions, but long-term memory specifically required high light conditions during the acquisition phase (familiarization trial) and was independent of the light level during retrieval (test trial). Additionally, long-term memory appeared to be independent of stress-mediated glucocorticoid release, as both low and high light produced similar levels of plasma corticosterone, which further did not correlate with subsequent memory performance. Finally, both short-term and long-term memory showed no savings between repeated experiments suggesting that this novel object recognition paradigm may be useful for longitudinal studies, particularly when investigating treatments to stabilize or enhance weak memories in neurodegenerative diseases or during age-related cognitive decline. Copyright © 2013 Elsevier Inc. All rights reserved.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Simulated Prosthetic Vision: The Benefits of Computer-Based Object Recognition and Localization.

PubMed

Macé, Marc J-M; Guivarch, Valérian; Denis, Grégoire; Jouffrais, Christophe

2015-07-01

Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for object recognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (object recognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method-that is, localization of targets of interest in the scene-may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Object Recognition Memory and the Rodent Hippocampus

ERIC Educational Resources Information Center

Broadbent, Nicola J.; Gaskin, Stephane; Squire, Larry R.; Clark, Robert E.

2010-01-01

In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

Verbal Memory Functioning in Adolescents and Young Adults with Costello Syndrome: Evidence for Relative Preservation in Recognition Memory

PubMed Central

Schwartz, David D.; Katzenstein, Jennifer M.; Hopkins, Elisabeth; Stabley, Deborah L.; Sol-Church, Katia; Gripp, Karen W.; Axelrad, Marni E.

2013-01-01

Costello syndrome (CS) is a rare genetic disorder caused by germline mutations in the HRAS proto-oncogene which belongs to the family of syndromes called rasopathies. HRAS plays a key role in synaptic long-term potentiation (LTP) and memory formation. Prior research has found impaired recall memory in CS despite enhancement in LTP that would predict memory preservation. Based on findings in other rasopathies, we hypothesized that the memory deficit in CS would be specific to recall, and that recognition memory would show relative preservation. Memory was tested using word-list learning and story memory tasks with both recall and recognition trials, a design that allowed us to examine these processes separately. Participants were 11 adolescents and young adults with molecularly confirmed CS, all of whom fell in the mild to moderate range of intellectual disability. Results indicated a clear dissociation between verbal recall, which was impaired (M = 69 ± 14), and recognition memory, which was relatively intact (M = 86 ± 14). Story recognition was highly correlated with listening comprehension (r = .986), which also fell in the low-average range (M = 80 ± 12.9). Performance on other measures of linguistic ability and academic skills was impaired. The findings suggest relatively preserved recognition memory that also provides some support for verbal comprehension. This is the first report of relatively normal performance in a cognitive domain in CS. Further research is needed to better understand the mechanisms by which altered RAS-MAPK signaling affects neuronal plasticity and memory processes in the brain. PMID:23918324

Age-specific effects of voluntary exercise on memory and the older brain.

PubMed

Siette, Joyce; Westbrook, R Frederick; Cotman, Carl; Sidhu, Kuldip; Zhu, Wanlin; Sachdev, Perminder; Valenzuela, Michael J

2013-03-01

Physical exercise in early adulthood and mid-life improves cognitive function and enhances brain plasticity, but the effects of commencing exercise in late adulthood are not well-understood. We investigated the effects of voluntary exercise in the restoration of place recognition memory in aged rats and examined hippocampal changes of synaptic density and neurogenesis. We found a highly selective age-related deficit in place recognition memory that is stable across retest sessions and correlates strongly with loss of hippocampal synapses. Additionally, 12 weeks of voluntary running at 20 months of age removed the deficit in the hippocampally dependent place recognition memory. Voluntary running restored presynaptic density in the dentate gyrus and CA3 hippocampal subregions in aged rats to levels beyond those observed in younger animals, in which exercise had no functional or synaptic effects. By contrast, hippocampal neurogenesis, a possible memory-related mechanism, increased in both young and aged rats after physical exercise but was not linked with performance in the place recognition task. We used graph-based network analysis based on synaptic covariance patterns to characterize efficient intrahippocampal connectivity. This analysis revealed that voluntary running completely reverses the profound degradation of hippocampal network efficiency that accompanies sedentary aging. Furthermore, at an individual animal level, both overall hippocampal presynaptic density and subregional connectivity independently contribute to prediction of successful place recognition memory performance. Our findings emphasize the unique synaptic effects of exercise on the aged brain and their specific relevance to a hippocampally based memory system for place recognition. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Social Competence Intervention for Youth with Asperger Syndrome and High-Functioning Autism: An Initial Investigation

ERIC Educational Resources Information Center

Stichter, Janine P.; Herzog, Melissa J.; Visovsky, Karen; Schmidt, Carla; Randolph, Jena; Schultz, Tia; Gage, Nicholas

2010-01-01

Individuals with high functioning autism (HFA) or Asperger Syndrome (AS) exhibit difficulties in the knowledge or correct performance of social skills. This subgroup's social difficulties appear to be associated with deficits in three social cognition processes: theory of mind, emotion recognition and executive functioning. The current study…

Face Configuration Accuracy and Processing Speed among Adults with High-Functioning Autism Spectrum Disorders

ERIC Educational Resources Information Center

Faja, Susan; Webb, Sara Jane; Merkle, Kristen; Aylward, Elizabeth; Dawson, Geraldine

2009-01-01

The present study investigates the accuracy and speed of face processing employed by high-functioning adults with autism spectrum disorders (ASDs). Two behavioral experiments measured sensitivity to distances between features and face recognition when performance depended on holistic versus featural information. Results suggest adults with ASD…

Facial recognition performance of female inmates as a result of sexual assault history.

PubMed

Islam-Zwart, Kayleen A; Heath, Nicole M; Vik, Peter W

2005-06-01

This study examined the effect of sexual assault history on facial recognition performance. Gender of facial stimuli and posttraumatic stress disorder (PTSD) symptoms also were expected to influence performance. Fifty-six female inmates completed an interview and the Wechsler Memory Scale-Third Edition Faces I and Faces II subtests (Wechsler, 1997). Women with a sexual assault exhibited better immediate and delayed facial recognition skills than those with no assault history. There were no differences in performance based on the gender of faces or PTSD diagnosis. Immediate facial recognition was correlated with report of PTSD symptoms. Findings provide greater insight into women's reactions to, and the uniqueness of, the trauma of sexual victimization.

Robust wafer identification recognition based on asterisk-shape filter and high-low score comparison method.

PubMed

Hsu, Wei-Chih; Yu, Tsan-Ying; Chen, Kuan-Liang

2009-12-10

Wafer identifications (wafer ID) can be used to identify wafers from each other so that wafer processing can be traced easily. Wafer ID recognition is one of the problems of optical character recognition. The process to recognize wafer IDs is similar to that used in recognizing car license-plate characters. However, due to some unique characteristics, such as the irregular space between two characters and the unsuccessive strokes of wafer ID, it will not get a good result to recognize wafer ID by directly utilizing the approaches used in car license-plate character recognition. Wafer ID scratches are engraved by a laser scribe almost along the following four fixed directions: horizontal, vertical, plus 45 degrees , and minus 45 degrees orientations. The closer to the center line of a wafer ID scratch, the higher the gray level will be. These and other characteristics increase the difficulty to recognize the wafer ID. In this paper a wafer ID recognition scheme based on an asterisk-shape filter and a high-low score comparison method is proposed to cope with the serious influence of uneven luminance and make recognition more efficiently. Our proposed approach consists of some processing stages. Especially in the final recognition stage, a template-matching method combined with stroke analysis is used as a recognizing scheme. This is because wafer IDs are composed of Semiconductor Equipment and Materials International (SEMI) standard Arabic numbers and English alphabets, and thus the template ID images are easy to obtain. Furthermore, compared with the approach that requires prior training, such as a support vector machine, which often needs a large amount of training image samples, no prior training is required for our approach. The testing results show that our proposed scheme can efficiently and correctly segment out and recognize the wafer ID with high performance.

An evaluation of the effects of high visual taskload on the separate behaviors involved in complex monitoring performance.

DOT National Transportation Integrated Search

1988-01-01

Operational monitoring situations, in contrast to typical laboratory vigilance tasks, generally involve more than just stimulus detection and recognition. They frequently involve complex multidimensional discriminations, interpretations of significan...

Strategies for Maximizing Speech-Recognition Performance: Adults with Mild to Moderately Severe Sensorineural Hearing Loss. EBP Briefs. Volume 11, Issue 4

ERIC Educational Resources Information Center

Donai, Jeremy J.; Schwartz, Jeremy C.

2016-01-01

Clinical Question: What high-frequency amplification strategy maximizes speechrecognition performance among adult hearing-impaired listeners with mild sloping to moderately severe sensorineural hearing loss? Method: Quick response review. Study Sources: EBSCO, PubMed, Google Scholar, as well as journals from the American Speech-Language-Hearing…

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

Human region segmentation and description methods for domiciliary healthcare monitoring using chromatic methodology

NASA Astrophysics Data System (ADS)

Al-Temeemy, Ali A.

2018-03-01

A descriptor is proposed for use in domiciliary healthcare monitoring systems. The descriptor is produced from chromatic methodology to extract robust features from the monitoring system's images. It has superior discrimination capabilities, is robust to events that normally disturb monitoring systems, and requires less computational time and storage space to achieve recognition. A method of human region segmentation is also used with this descriptor. The performance of the proposed descriptor was evaluated using experimental data sets, obtained through a series of experiments performed in the Centre for Intelligent Monitoring Systems, University of Liverpool. The evaluation results show high recognition performance for the proposed descriptor in comparison to traditional descriptors, such as moments invariant. The results also show the effectiveness of the proposed segmentation method regarding distortion effects associated with domiciliary healthcare systems.

Clinical implications of word recognition differences in earphone and aided conditions

PubMed Central

McRackan, Theodore R.; Ahlstrom, Jayne B.; Clinkscales, William B.; Meyer, Ted A.; Dubno, Judy R

2017-01-01

Objective To compare word recognition scores for adults with hearing loss measured using earphones and in the sound field without and with hearing aids (HA) Study design Independent review of pre-surgical audiological data from an active middle ear implant (MEI) FDA clinical trial Setting Multicenter prospective FDA clinical trial Patients Ninety-four adult HA users Interventions/Main outcomes measured Pre-operative earphone, unaided and aided pure tone thresholds, word recognition scores, and speech intelligibility index. Results We performed an independent review of pre-surgical audiological data from a MEI FDA trial and compared unaided and aided word recognition scores with participants’ HAs fit according to the NAL-R algorithm. For 52 participants (55.3%), differences in scores between earphone and aided conditions were >10%; for 33 participants (35.1%), earphone scores were higher by 10% or more than aided scores. These participants had significantly higher pure tone thresholds at 250 Hz, 500 Hz, and 1000 Hz), higher pure tone averages, higher speech recognition thresholds, (and higher earphone speech levels (p=0.002). No significant correlation was observed between word recognition scores measured with earphones and with hearing aids (r=.14; p=0.16), whereas a moderately high positive correlation was observed between unaided and aided word recognition (r=0.68; p<0.001). Conclusion Results of the these analyses do not support the common clinical practice of using word recognition scores measured with earphones to predict aided word recognition or hearing aid benefit. Rather, these results provide evidence supporting the measurement of aided word recognition in patients who are considering hearing aids. PMID:27631832

Phonological mismatch makes aided speech recognition in noise cognitively taxing.

PubMed

Rudner, Mary; Foo, Catharina; Rönnberg, Jerker; Lunner, Thomas

2007-12-01

The working memory framework for Ease of Language Understanding predicts that speech processing becomes more effortful, thus requiring more explicit cognitive resources, when there is mismatch between speech input and phonological representations in long-term memory. To test this prediction, we changed the compression release settings in the hearing instruments of experienced users and allowed them to train for 9 weeks with the new settings. After training, aided speech recognition in noise was tested with both the trained settings and orthogonal settings. We postulated that training would lead to acclimatization to the trained setting, which in turn would involve establishment of new phonological representations in long-term memory. Further, we postulated that after training, testing with orthogonal settings would give rise to phonological mismatch, associated with more explicit cognitive processing. Thirty-two participants (mean=70.3 years, SD=7.7) with bilateral sensorineural hearing loss (pure-tone average=46.0 dB HL, SD=6.5), bilaterally fitted for more than 1 year with digital, two-channel, nonlinear signal processing hearing instruments and chosen from the patient population at the Linköping University Hospital were randomly assigned to 9 weeks training with new, fast (40 ms) or slow (640 ms), compression release settings in both channels. Aided speech recognition in noise performance was tested according to a design with three within-group factors: test occasion (T1, T2), test setting (fast, slow), and type of noise (unmodulated, modulated) and one between-group factor: experience setting (fast, slow) for two types of speech materials-the highly constrained Hagerman sentences and the less-predictable Hearing in Noise Test (HINT). Complex cognitive capacity was measured using the reading span and letter monitoring tests. PREDICTION: We predicted that speech recognition in noise at T2 with mismatched experience and test settings would be associated with more explicit cognitive processing and thus stronger correlations with complex cognitive measures, as well as poorer performance if complex cognitive capacity was exceeded. Under mismatch conditions, stronger correlations were found between performance on speech recognition with the Hagerman sentences and reading span, along with poorer speech recognition for participants with low reading span scores. No consistent mismatch effect was found with HINT. The mismatch prediction generated by the working memory framework for Ease of Language Understanding is supported for speech recognition in noise with the highly constrained Hagerman sentences but not the less-predictable HINT.

Hierarchically Structured Non-Intrusive Sign Language Recognition. Chapter 2

NASA Technical Reports Server (NTRS)

Zieren, Jorg; Zieren, Jorg; Kraiss, Karl-Friedrich

2007-01-01

This work presents a hierarchically structured approach at the nonintrusive recognition of sign language from a monocular frontal view. Robustness is achieved through sophisticated localization and tracking methods, including a combined EM/CAMSHIFT overlap resolution procedure and the parallel pursuit of multiple hypotheses about hands position and movement. This allows handling of ambiguities and automatically corrects tracking errors. A biomechanical skeleton model and dynamic motion prediction using Kalman filters represents high level knowledge. Classification is performed by Hidden Markov Models. 152 signs from German sign language were recognized with an accuracy of 97.6%.

Frontotemporal Functional Connectivity and Executive Functions Contribute to Episodic Memory Performance

PubMed Central

Blankenship, Tashauna L.; O'Neill, Meagan; Deater-Deckard, Kirby; Diana, Rachel A.; Bell, Martha Ann

2016-01-01

The contributions of hemispheric-specific electrophysiology (electroencephalogram or EEG) and independent executive functions (inhibitory control, working memory, cognitive flexibility) to episodic memory performance were examined using abstract paintings. Right hemisphere frontotemporal functional connectivity during encoding and retrieval, measured via EEG alpha coherence, statistically predicted performance on recency but not recognition judgments for the abstract paintings. Theta coherence, however, did not predict performance. Likewise, cognitive flexibility statistically predicted performance on recency judgments, but not recognition. These findings suggest that recognition and recency operate via separate electrophysiological and executive mechanisms. PMID:27388478

Road sign recognition with fuzzy adaptive pre-processing models.

PubMed

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

PubMed Central

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

N200 and P300 as orthogonal and integrable indicators of distinct awareness and recognition processes in memory detection.

PubMed

Hu, Xiaoqing; Pornpattananangkul, Narun; Rosenfeld, J Peter

2013-05-01

In an event-related potential (ERP)-based concealed information test (CIT), we investigated the effect of manipulated awareness of concealed information on the ERPs. Participants either committed a mock crime or not (guilty vs. innocent) before the CIT, and received feedback regarding either specific (high awareness) or general (low awareness) task performance during the CIT. We found that awareness and recognition of the crime-relevant information differentially influenced the frontal-central N200 and parietal P300: Probe elicited a larger N200 than irrelevant only when guilty participants were in the high awareness condition, whereas the P300 was mainly responsive to information recognition. No N200-P300 correlation was found, allowing for a combined measure of both yielding the highest detection efficiency in the high awareness group (AUC = .91). Finally, a color-naming Stroop task following the CIT revealed that guilty participants showed larger interference effects than innocent participants, suggesting that the former expended more attentional resources during the CIT. Copyright © 2013 Society for Psychophysiological Research.

Entity recognition in the biomedical domain using a hybrid approach.

PubMed

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

Stability of facial emotion recognition performance in bipolar disorder.

PubMed

Martino, Diego J; Samamé, Cecilia; Strejilevich, Sergio A

2016-09-30

The aim of this study was to assess the performance in emotional processing over time in a sample of euthymic patients with bipolar disorder (BD). Performance in the facial recognition of the six basic emotions (surprise, anger, sadness, happiness, disgust, and fear) did not change during a follow-up period of almost 7 years. These preliminary results suggest that performance in facial emotion recognition might be stable over time in BD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Continuous Chinese sign language recognition with CNN-LSTM

NASA Astrophysics Data System (ADS)

Yang, Su; Zhu, Qing

2017-07-01

The goal of sign language recognition (SLR) is to translate the sign language into text, and provide a convenient tool for the communication between the deaf-mute and the ordinary. In this paper, we formulate an appropriate model based on convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) network, in order to accomplish the continuous recognition work. With the strong ability of CNN, the information of pictures captured from Chinese sign language (CSL) videos can be learned and transformed into vector. Since the video can be regarded as an ordered sequence of frames, LSTM model is employed to connect with the fully-connected layer of CNN. As a recurrent neural network (RNN), it is suitable for sequence learning tasks with the capability of recognizing patterns defined by temporal distance. Compared with traditional RNN, LSTM has performed better on storing and accessing information. We evaluate this method on our self-built dataset including 40 daily vocabularies. The experimental results show that the recognition method with CNN-LSTM can achieve a high recognition rate with small training sets, which will meet the needs of real-time SLR system.

Quantitative expression and immunogenicity of MAGE-3 and -6 in upper aerodigestive tract cancer.

PubMed

Filho, Pedro A Andrade; López-Albaitero, Andrés; Xi, Liqiang; Gooding, William; Godfrey, Tony; Ferris, Robert L

2009-10-15

The MAGE antigens are frequently expressed cancer vaccine targets. However, quantitative analysis of MAGE expression in upper aerodigestive tract (UADT) tumor cells and its association with T-cell recognition has not been performed, hindering the selection of appropriate candidates for MAGE-specific immunotherapy. Using quantitative RT-PCR (QRT-PCR), we evaluated the expression of MAGE-3/6 in 65 UADT cancers, 48 normal samples from tumor matched sites and 7 HLA-A*0201+ squamous cell carcinoma of the head and neck (SCCHN) cell lines. Expression results were confirmed using Western blot. HLA-A*0201:MAGE-3- (271-279) specific cytotoxic T lymphocytes (MAGE-CTL) from SCCHN patients and healthy donors showed that MAGE-3/6 expression was highly associated with CTL recognition in vitro. On the basis of the MAGE-3/6 expression, we could identify 31 (47%) of the 65 UADT tumors, which appeared to express MAGE-3/6 at levels that correlated with efficient CTL recognition. To confirm that the level of MAGE-3 expression was responsible for CTL recognition, 2 MAGE-3/6 mRNA(high) SCCHN cell lines, PCI-13 and PCI-30, were subjected to MAGE-3/6-specific knockdown. RNAi-transfected cells showed that MAGE expression and MAGE-CTL recognition were significantly reduced. Furthermore, treatment of cells expressing low MAGE-3/6 mRNA with a demethylating agent, 5-aza-2'-deoxycytidine (DAC), increased the expression of MAGE-3/6 and CTL recognition. Thus, using QRT-PCR UADT cancers frequently express MAGE-3/6 at levels sufficient for CTL recognition, supporting the use of a QRT-PCR-based assay for the selection of candidates likely to respond to MAGE-3/6 immunotherapy. Demethylating agents could increase the number of patients amenable for targeting epigenetically modified tumor antigens in vaccine trials.

Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography.

PubMed

Stevenson, Paul G; Mnatsakanyan, Mariam; Guiochon, Georges; Shalliker, R Andrew

2010-07-01

An algorithm was developed for 2DHPLC that automated the process of peak recognition, measuring their retention times, and then subsequently plotting the information in a two-dimensional retention plane. Following the recognition of peaks, the software then performed a series of statistical assessments of the separation performance, measuring for example, correlation between dimensions, peak capacity and the percentage of usage of the separation space. Peak recognition was achieved by interpreting the first and second derivatives of each respective one-dimensional chromatogram to determine the 1D retention times of each solute and then compiling these retention times for each respective fraction 'cut'. Due to the nature of comprehensive 2DHPLC adjacent cut fractions may contain peaks common to more than one cut fraction. The algorithm determined which components were common in adjacent cuts and subsequently calculated the peak maximum profile by interpolating the space between adjacent peaks. This algorithm was applied to the analysis of a two-dimensional separation of an apple flesh extract separated in a first dimension comprising a cyano stationary phase and an aqueous/THF mobile phase as the first dimension and a second dimension comprising C18-Hydro with an aqueous/MeOH mobile phase. A total of 187 peaks were detected.

Towards automated assistance for operating home medical devices.

PubMed

Gao, Zan; Detyniecki, Marcin; Chen, Ming-Yu; Wu, Wen; Hauptmann, Alexander G; Wactlar, Howard D

2010-01-01

To detect errors when subjects operate a home medical device, we observe them with multiple cameras. We then perform action recognition with a robust approach to recognize action information based on explicitly encoding motion information. This algorithm detects interest points and encodes not only their local appearance but also explicitly models local motion. Our goal is to recognize individual human actions in the operations of a home medical device to see if the patient has correctly performed the required actions in the prescribed sequence. Using a specific infusion pump as a test case, requiring 22 operation steps from 6 action classes, our best classifier selects high likelihood action estimates from 4 available cameras, to obtain an average class recognition rate of 69%.

Practical automatic Arabic license plate recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

Face recognition system for set-top box-based intelligent TV.

PubMed

Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

2014-11-18

Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user registration stage and multi-level local binary pattern matching. Experimental results indicate that the recall; precision; and genuine acceptance rate were about 95.7%; 96.2%; and 90.2%, respectively.

It doesn't matter what you say: FMRI correlates of voice learning and recognition independent of speech content.

PubMed

Zäske, Romi; Awwad Shiekh Hasan, Bashar; Belin, Pascal

2017-09-01

Listeners can recognize newly learned voices from previously unheard utterances, suggesting the acquisition of high-level speech-invariant voice representations during learning. Using functional magnetic resonance imaging (fMRI) we investigated the anatomical basis underlying the acquisition of voice representations for unfamiliar speakers independent of speech, and their subsequent recognition among novel voices. Specifically, listeners studied voices of unfamiliar speakers uttering short sentences and subsequently classified studied and novel voices as "old" or "new" in a recognition test. To investigate "pure" voice learning, i.e., independent of sentence meaning, we presented German sentence stimuli to non-German speaking listeners. To disentangle stimulus-invariant and stimulus-dependent learning, during the test phase we contrasted a "same sentence" condition in which listeners heard speakers repeating the sentences from the preceding study phase, with a "different sentence" condition. Voice recognition performance was above chance in both conditions although, as expected, performance was higher for same than for different sentences. During study phases activity in the left inferior frontal gyrus (IFG) was related to subsequent voice recognition performance and same versus different sentence condition, suggesting an involvement of the left IFG in the interactive processing of speaker and speech information during learning. Importantly, at test reduced activation for voices correctly classified as "old" compared to "new" emerged in a network of brain areas including temporal voice areas (TVAs) of the right posterior superior temporal gyrus (pSTG), as well as the right inferior/middle frontal gyrus (IFG/MFG), the right medial frontal gyrus, and the left caudate. This effect of voice novelty did not interact with sentence condition, suggesting a role of temporal voice-selective areas and extra-temporal areas in the explicit recognition of learned voice identity, independent of speech content. Copyright © 2017 Elsevier Ltd. All rights reserved.

Standard-Chinese Lexical Neighborhood Test in normal-hearing young children.

PubMed

Liu, Chang; Liu, Sha; Zhang, Ning; Yang, Yilin; Kong, Ying; Zhang, Luo

2011-06-01

The purposes of the present study were to establish the Standard-Chinese version of Lexical Neighborhood Test (LNT) and to examine the lexical and age effects on spoken-word recognition in normal-hearing children. Six lists of monosyllabic and six lists of disyllabic words (20 words/list) were selected from the database of daily speech materials for normal-hearing (NH) children of ages 3-5 years. The lists were further divided into "easy" and "hard" halves according to the word frequency and neighborhood density in the database based on the theory of Neighborhood Activation Model (NAM). Ninety-six NH children (age ranged between 4.0 and 7.0 years) were divided into three different age groups of 1-year intervals. Speech-perception tests were conducted using the Standard-Chinese monosyllabic and disyllabic LNT. The inter-list performance was found to be equivalent and inter-rater reliability was high with 92.5-95% consistency. Results of word-recognition scores showed that the lexical effects were all significant. Children scored higher with disyllabic words than with monosyllabic words. "Easy" words scored higher than "hard" words. The word-recognition performance also increased with age in each lexical category. A multiple linear regression analysis showed that neighborhood density, age, and word frequency appeared to have increasingly more contributions to Chinese word recognition. The results of the present study indicated that performances of Chinese word recognition were influenced by word frequency, age, and neighborhood density, with word frequency playing a major role. These results were consistent with those in other languages, supporting the application of NAM in the Chinese language. The development of Standard-Chinese version of LNT and the establishment of a database of children of 4-6 years old can provide a reliable means for spoken-word recognition test in children with hearing impairment. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

ERIC Educational Resources Information Center

Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger

2012-01-01

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences among individuals who contributed to the English…

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

PubMed Central

Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger

2011-01-01

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences between individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly four million word recognition (speeded pronunciation and lexical decision) trials from over 1,200 participants. We observed considerable within- and between-session reliability across distinct sets of items, in terms of overall mean response time (RT), RT distributional characteristics, diffusion model parameters (Ratcliff, Gomez, & McKoon, 2004), and sensitivity to underlying lexical dimensions. This indicates reliably detectable individual differences in word recognition performance. In addition, higher vocabulary knowledge was associated with faster, more accurate word recognition performance, attenuated sensitivity to stimuli characteristics, and more efficient accumulation of information. Finally, in contrast to suggestions in the literature, we did not find evidence that individuals were trading-off in their utilization of lexical and nonlexical information. PMID:21728459

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

Negative words enhance recognition in nonclinical high dissociators: An fMRI study.

PubMed

de Ruiter, Michiel B; Veltman, Dick J; Phaf, R Hans; van Dyck, Richard

2007-08-01

Memory encoding and retrieval were studied in a nonclinical sample of participants that differed in the amount of reported dissociative experiences (trait dissociation). Behavioral as well as functional imaging (fMRI) indices were used as convergent measures of memory functioning. In a deep vs. shallow encoding paradigm, the influence of dissociative style on elaborative and avoidant encoding was studied, respectively. Furthermore, affectively neutral and negative words were presented, to test whether the effects of dissociative tendencies on memory functioning depended on the affective valence of the stimulus material. Results showed that (a) deep encoding of negative vs. neutral stimuli was associated with higher levels of semantic elaboration in high than in low dissociators, as indicated by increased levels of activity in hippocampus and prefrontal cortex during encoding and higher memory performance during recognition, (b) high dissociators were generally characterized by higher levels of conscious recollection as indicated by increased activity of the hippocampus and posterior parietal areas during recognition, (c) nonclinical high dissociators were not characterized by an avoidant encoding style. These results support the notion that trait dissociation in healthy individuals is associated with high levels of elaborative encoding, resulting in high levels of conscious recollection. These abilities, in addition, seem to depend on the salience of the presented stimulus material.

Key features for ATA / ATR database design in missile systems

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2017-05-01

Automatic target acquisition (ATA) and automatic target recognition (ATR) are two vital tasks for missile systems, and having a robust detection and recognition algorithm is crucial for overall system performance. In order to have a robust target detection and recognition algorithm, an extensive image database is required. Automatic target recognition algorithms use the database of images in training and testing steps of algorithm. This directly affects the recognition performance, since the training accuracy is driven by the quality of the image database. In addition, the performance of an automatic target detection algorithm can be measured effectively by using an image database. There are two main ways for designing an ATA / ATR database. The first and easy way is by using a scene generator. A scene generator can model the objects by considering its material information, the atmospheric conditions, detector type and the territory. Designing image database by using a scene generator is inexpensive and it allows creating many different scenarios quickly and easily. However the major drawback of using a scene generator is its low fidelity, since the images are created virtually. The second and difficult way is designing it using real-world images. Designing image database with real-world images is a lot more costly and time consuming; however it offers high fidelity, which is critical for missile algorithms. In this paper, critical concepts in ATA / ATR database design with real-world images are discussed. Each concept is discussed in the perspective of ATA and ATR separately. For the implementation stage, some possible solutions and trade-offs for creating the database are proposed, and all proposed approaches are compared to each other with regards to their pros and cons.

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Good Practices for Learning to Recognize Actions Using FV and VLAD.

PubMed

Wu, Jianxin; Zhang, Yu; Lin, Weiyao

2016-12-01

High dimensional representations such as Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD) have shown state-of-the-art accuracy for action recognition in videos. The high dimensionality, on the other hand, also causes computational difficulties when scaling up to large-scale video data. This paper makes three lines of contributions to learning to recognize actions using high dimensional representations. First, we reviewed several existing techniques that improve upon FV or VLAD in image classification, and performed extensive empirical evaluations to assess their applicability for action recognition. Our analyses of these empirical results show that normality and bimodality are essential to achieve high accuracy. Second, we proposed a new pooling strategy for VLAD and three simple, efficient, and effective transformations for both FV and VLAD. Both proposed methods have shown higher accuracy than the original FV/VLAD method in extensive evaluations. Third, we proposed and evaluated new feature selection and compression methods for the FV and VLAD representations. This strategy uses only 4% of the storage of the original representation, but achieves comparable or even higher accuracy. Based on these contributions, we recommend a set of good practices for action recognition in videos for practitioners in this field.

Antigen clasping by two antigen-binding sites of an exceptionally specific antibody for histone methylation

DOE PAGES

Hattori, Takamitsu; Lai, Darson; Dementieva, Irina S.; ...

2016-02-09

Antibodies have a well-established modular architecture wherein the antigen-binding site residing in the antigen-binding fragment (Fab or Fv) is an autonomous and complete unit for antigen recognition. Here, we describe antibodies departing from this paradigm. We developed recombinant antibodies to trimethylated lysine residues on histone H3, important epigenetic marks and challenging targets for molecular recognition. Quantitative characterization demonstrated their exquisite specificity and high affinity, and they performed well in common epigenetics applications. Surprisingly, crystal structures and biophysical analyses revealed that two antigen-binding sites of these antibodies form a head-to-head dimer and cooperatively recognize the antigen in the dimer interface. Thismore » “antigen clasping” produced an expansive interface where trimethylated Lys bound to an unusually extensive aromatic cage in one Fab and the histone N terminus to a pocket in the other, thereby rationalizing the high specificity. A long-neck antibody format with a long linker between the antigen-binding module and the Fc region facilitated antigen clasping and achieved both high specificity and high potency. Antigen clasping substantially expands the paradigm of antibody–antigen recognition and suggests a strategy for developing extremely specific antibodies.« less

Antigen clasping by two antigen-binding sites of an exceptionally specific antibody for histone methylation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hattori, Takamitsu; Lai, Darson; Dementieva, Irina S.

Antibodies have a well-established modular architecture wherein the antigen-binding site residing in the antigen-binding fragment (Fab or Fv) is an autonomous and complete unit for antigen recognition. Here, we describe antibodies departing from this paradigm. We developed recombinant antibodies to trimethylated lysine residues on histone H3, important epigenetic marks and challenging targets for molecular recognition. Quantitative characterization demonstrated their exquisite specificity and high affinity, and they performed well in common epigenetics applications. Surprisingly, crystal structures and biophysical analyses revealed that two antigen-binding sites of these antibodies form a head-to-head dimer and cooperatively recognize the antigen in the dimer interface. Thismore » “antigen clasping” produced an expansive interface where trimethylated Lys bound to an unusually extensive aromatic cage in one Fab and the histone N terminus to a pocket in the other, thereby rationalizing the high specificity. A long-neck antibody format with a long linker between the antigen-binding module and the Fc region facilitated antigen clasping and achieved both high specificity and high potency. Antigen clasping substantially expands the paradigm of antibody–antigen recognition and suggests a strategy for developing extremely specific antibodies.« less

Compact holographic optical neural network system for real-time pattern recognition

NASA Astrophysics Data System (ADS)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Separation and enrichment of trace ractopamine in biological samples by uniformly-sized molecularly imprinted polymers

PubMed Central

Li, Ya; Fu, Qiang; Liu, Meng; Jiao, Yuan-Yuan; Du, Wei; Yu, Chong; Liu, Jing; Chang, Chun; Lu, Jian

2012-01-01

In order to prepare a high capacity packing material for solid-phase extraction with specific recognition ability of trace ractopamine in biological samples, uniformly-sized, molecularly imprinted polymers (MIPs) were prepared by a multi-step swelling and polymerization method using methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a cross-linker, and toluene as a porogen respectively. Scanning electron microscope and specific surface area were employed to identify the characteristics of MIPs. Ultraviolet spectroscopy, Fourier transform infrared spectroscopy, Scatchard analysis and kinetic study were performed to interpret the specific recognition ability and the binding process of MIPs. The results showed that, compared with other reports, MIPs synthetized in this study showed high adsorption capacity besides specific recognition ability. The adsorption capacity of MIPs was 0.063 mmol/g at 1 mmol/L ractopamine concentration with the distribution coefficient 1.70. The resulting MIPs could be used as solid-phase extraction materials for separation and enrichment of trace ractopamine in biological samples. PMID:29403774

Performance and Usage of Biometrics in a Testbed Environment for Tactical Purposes

DTIC Science & Technology

2006-12-01

19 c. Facial Recognition ..................................................................20...geometry, iris recognition, and facial recognition (Layman’s, 2005). Behavioral biometrics can be described not as a physical characteristic, but are...are at: • Correction facilities • Department of Motor Vehicle • Military checkpoints • POW facilities c. Facial Recognition Facial recognition is

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

PubMed Central

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan

2015-01-01

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines. PMID:26345990

Relaxing decision criteria does not improve recognition memory in amnesic patients.

PubMed

Reber, P J; Squire, L R

1999-05-01

An important question about the organization of memory is whether information available in non-declarative memory can contribute to performance on tasks of declarative memory. Dorfman, Kihlstrom, Cork, and Misiaszek (1995) described a circumstance in which the phenomenon of priming might benefit recognition memory performance. They reported that patients receiving electroconvulsive therapy improved their recognition performance when they were encouraged to relax their criteria for endorsing test items as familiar. It was suggested that priming improved recognition by making information available about the familiarity of test items. In three experiments, we sought unsuccessfully to reproduce this phenomenon in amnesic patients. In Experiment 3, we reproduced the methods and procedure used by Dorfman et al. but still found no evidence for improved recognition memory following the manipulation of decision criteria. Although negative findings have their own limitations, our findings suggest that the phenomenon reported by Dorfman et al. does not generalize well. Our results agree with several recent findings that suggest that priming is independent of recognition memory and does not contribute to recognition memory scores.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

Acquired prosopagnosia without word recognition deficits.

PubMed

Susilo, Tirta; Wright, Victoria; Tree, Jeremy J; Duchaine, Bradley

2015-01-01

It has long been suggested that face recognition relies on specialized mechanisms that are not involved in visual recognition of other object categories, including those that require expert, fine-grained discrimination at the exemplar level such as written words. But according to the recently proposed many-to-many theory of object recognition (MTMT), visual recognition of faces and words are carried out by common mechanisms [Behrmann, M., & Plaut, D. C. ( 2013 ). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in Cognitive Sciences, 17, 210-219]. MTMT acknowledges that face and word recognition are lateralized, but posits that the mechanisms that predominantly carry out face recognition still contribute to word recognition and vice versa. MTMT makes a key prediction, namely that acquired prosopagnosics should exhibit some measure of word recognition deficits. We tested this prediction by assessing written word recognition in five acquired prosopagnosic patients. Four patients had lesions limited to the right hemisphere while one had bilateral lesions with more pronounced lesions in the right hemisphere. The patients completed a total of seven word recognition tasks: two lexical decision tasks and five reading aloud tasks totalling more than 1200 trials. The performances of the four older patients (3 female, age range 50-64 years) were compared to those of 12 older controls (8 female, age range 56-66 years), while the performances of the younger prosopagnosic (male, 31 years) were compared to those of 14 younger controls (9 female, age range 20-33 years). We analysed all results at the single-patient level using Crawford's t-test. Across seven tasks, four prosopagnosics performed as quickly and accurately as controls. Our results demonstrate that acquired prosopagnosia can exist without word recognition deficits. These findings are inconsistent with a key prediction of MTMT. They instead support the hypothesis that face recognition is carried out by specialized mechanisms that do not contribute to recognition of written words.

Connecting High School Physics Experiences, Outcome Expectations, Physics Identity, and Physics Career Choice: A Gender Study

ERIC Educational Resources Information Center

Hazari, Zahra; Sonnert, Gerhard; Sadler, Philip M.; Shanahan, Marie-Claire

2010-01-01

This study explores how students' physics identities are shaped by their experiences in high school physics classes and by their career outcome expectations. The theoretical framework focuses on physics identity and includes the dimensions of student performance, competence, recognition by others, and interest. Drawing data from the Persistence…

Towards Smart Homes Using Low Level Sensory Data

PubMed Central

Khattak, Asad Masood; Truc, Phan Tran Ho; Hung, Le Xuan; Vinh, La The; Dang, Viet-Hung; Guan, Donghai; Pervez, Zeeshan; Han, Manhyung; Lee, Sungyoung; Lee, Young-Koo

2011-01-01

Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules. PMID:22247682

On the three-quarter view advantage of familiar object recognition.

PubMed

Nonose, Kohei; Niimi, Ryosuke; Yokosawa, Kazuhiko

2016-11-01

A three-quarter view, i.e., an oblique view, of familiar objects often leads to a higher subjective goodness rating when compared with other orientations. What is the source of the high goodness for oblique views? First, we confirmed that object recognition performance was also best for oblique views around 30° view, even when the foreshortening disadvantage of front- and side-views was minimized (Experiments 1 and 2). In Experiment 3, we measured subjective ratings of view goodness and two possible determinants of view goodness: familiarity of view, and subjective impression of three-dimensionality. Three-dimensionality was measured as the subjective saliency of visual depth information. The oblique views were rated best, most familiar, and as approximating greatest three-dimensionality on average; however, the cluster analyses showed that the "best" orientation systematically varied among objects. We found three clusters of objects: front-preferred objects, oblique-preferred objects, and side-preferred objects. Interestingly, recognition performance and the three-dimensionality rating were higher for oblique views irrespective of the clusters. It appears that recognition efficiency is not the major source of the three-quarter view advantage. There are multiple determinants and variability among objects. This study suggests that the classical idea that a canonical view has a unique advantage in object perception requires further discussion.

Size-Sensitive Perceptual Representations Underlie Visual and Haptic Object Recognition

PubMed Central

Craddock, Matt; Lawson, Rebecca

2009-01-01

A variety of similarities between visual and haptic object recognition suggests that the two modalities may share common representations. However, it is unclear whether such common representations preserve low-level perceptual features or whether transfer between vision and haptics is mediated by high-level, abstract representations. Two experiments used a sequential shape-matching task to examine the effects of size changes on unimodal and crossmodal visual and haptic object recognition. Participants felt or saw 3D plastic models of familiar objects. The two objects presented on a trial were either the same size or different sizes and were the same shape or different but similar shapes. Participants were told to ignore size changes and to match on shape alone. In Experiment 1, size changes on same-shape trials impaired performance similarly for both visual-to-visual and haptic-to-haptic shape matching. In Experiment 2, size changes impaired performance on both visual-to-haptic and haptic-to-visual shape matching and there was no interaction between the cost of size changes and direction of transfer. Together the unimodal and crossmodal matching results suggest that the same, size-specific perceptual representations underlie both visual and haptic object recognition, and indicate that crossmodal memory for objects must be at least partly based on common perceptual representations. PMID:19956685

Neural network-based recognition of whistlers on spectrograms detected by satellite

NASA Astrophysics Data System (ADS)

Conti, Livio

2016-04-01

We present a system to automatically recognize and classify the occurrence of whistler waves on spectrograms of electric field measurements performed by satellite. Whistlers - VLF waves generated by lightning, with a specific spectral dispersion relation - can induce precipitation of trapped Van Allen particles and have a role in the chemistry of some atmospheric components (mainly NOx). Moreover, it has also been suggested that the increase of the number of anomalous whistlers (i.e. whistlers with high value of dispersion constant) could be induced by disturbances in the Earth-ionosphere wave-guide, generated by seismo-electromagnetic emissions. On satellite, the recognition of whistlers asks for analyzing high-resolution spectrograms that cannot be downloaded to Earth, due to the limits of data transmission. For this reason, a real time identification and classification must be performed on satellite, by avoiding downloading all the unprocessed data. The procedure that we have developed is based on a Time Delay Neural Network (TDNN). The TDNN, proposed some years ago for speech recognition, can be fruitfully also applied in real-time analysis of electromagnetic spectrograms in order to detect phenomena characterized by a specific shape/signature such as those of the whistler waves. Some studies have been performed by the RNF experiment on board of the DEMETER satellite and our algorithm could be adopted on board of the satellite CSES (China Seismo-Electromagnetic Satellite), launch scheduled by the end of 2016. Moreover, the procedure can be also adopted to automatic analysis of whistlers detected on ground.

Theory of Mind, Emotion Recognition and Social Perception in Individuals at Clinical High Risk for Psychosis: findings from the NAPLS-2 cohort.

PubMed

Barbato, Mariapaola; Liu, Lu; Cadenhead, Kristin S; Cannon, Tyrone D; Cornblatt, Barbara A; McGlashan, Thomas H; Perkins, Diana O; Seidman, Larry J; Tsuang, Ming T; Walker, Elaine F; Woods, Scott W; Bearden, Carrie E; Mathalon, Daniel H; Heinssen, Robert; Addington, Jean

2015-09-01

Social cognition, the mental operations that underlie social interactions, is a major construct to investigate in schizophrenia. Impairments in social cognition are present before the onset of psychosis, and even in unaffected first-degree relatives, suggesting that social cognition may be a trait marker of the illness. In a large cohort of individuals at clinical high risk for psychosis (CHR) and healthy controls, three domains of social cognition (theory of mind, facial emotion recognition and social perception) were assessed to clarify which domains are impaired in this population. Six-hundred and seventy-five CHR individuals and 264 controls, who were part of the multi-site North American Prodromal Longitudinal Study, completed The Awareness of Social Inference Test , the Penn Emotion Recognition task , the Penn Emotion Differentiation task , and the Relationship Across Domains , measures of theory of mind, facial emotion recognition, and social perception, respectively. Social cognition was not related to positive and negative symptom severity, but was associated with age and IQ. CHR individuals demonstrated poorer performance on all measures of social cognition. However, after controlling for age and IQ, the group differences remained significant for measures of theory of mind and social perception, but not for facial emotion recognition. Theory of mind and social perception are impaired in individuals at CHR for psychosis. Age and IQ seem to play an important role in the arising of deficits in facial affect recognition. Future studies should examine the stability of social cognition deficits over time and their role, if any, in the development of psychosis.

Dichotic Word Recognition in Noise and the Right-Ear Advantage

ERIC Educational Resources Information Center

Roup, Christina M.

2011-01-01

Purpose: This study sought to compare dichotic right-ear advantages (REAs) of young adults to older adult data (C. M. Roup, T. L. Wiley, & R. H. Wilson, 2006) after matching for overall levels of recognition performance. Specifically, speech-spectrum noise was introduced in order to reduce dichotic recognition performance of young adults to a…

Integration of nonthematic details in pictures and passages.

PubMed

Viera, C L; Homa, D L

1991-01-01

Nonthematic details in naturalistic scenes were manipulated to produce four stimulus versions: color photos, black-white copies, and elaborated and unelaborated line drawings (Experiment 1); analogous verbal descriptions of each visual version were produced for Experiment 2. In Experiment 1, two or three different versions of a scene were presented in the mixed condition; the same version of the scene was repeated either two or three times in the same condition, and a 1-presentation control condition was also included. In Experiment 2, the same presentation conditions were used across different groups of subjects who either viewed the pictures or heard the descriptions. An old/new recognition test was given in which the nonstudied versions of the studied items were used as foils. Higher false recognition performances for the mixed condition were found for the visual materials in both experiments, and in the second experiment the verbal materials produced equivalently high levels of false recognition for both same and mixed conditions. Additionally, in Experiment 2 the patterns of performances across material conditions were differentially affected by the manipulation of detail in the four stimulus versions. These differences across materials suggest that the integration of semantically consistent details across temporally separable presentations is facilitated when the stimuli do not provide visual/physical attributes to enhance discrimination of different presentations. Further, the evidence derived from the visual scenes in both experiments indicates that the semantic schema abstracted from a picture is not the sole mediator of recognition performance.

Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder.

PubMed

Lind, Sophie E; Bowler, Dermot M

2009-09-01

This study investigated semantic and episodic memory in autism spectrum disorder (ASD), using a task which assessed recognition and self-other source memory. Children with ASD showed undiminished recognition memory but significantly diminished source memory, relative to age- and verbal ability-matched comparison children. Both children with and without ASD showed an "enactment effect", demonstrating significantly better recognition and source memory for self-performed actions than other-person-performed actions. Within the comparison group, theory-of-mind (ToM) task performance was significantly correlated with source memory, specifically for other-person-performed actions (after statistically controlling for verbal ability). Within the ASD group, ToM task performance was not significantly correlated with source memory (after controlling for verbal ability). Possible explanations for these relations between source memory and ToM are considered.

Cognitive and Neural Bases of Skilled Performance.

DTIC Science & Technology

1987-10-04

advantage is that this method is not computationally demanding, and model -specific analyses such as high -precision source localization with realistic...and a two- < " high -threshold model satisfy theoretical and pragmatic independence. Discrimination and bias measures from these two models comparing...recognition memory of patients with dementing diseases, amnesics, and normal controls. We found the two- high -threshold model to be more sensitive Lloyd

Age-related individual variability in memory performance is associated with amygdala-hippocampal circuit function and emotional pattern separation.

PubMed

Leal, Stephanie L; Noche, Jessica A; Murray, Elizabeth A; Yassa, Michael A

2017-01-01

While aging is generally associated with episodic memory decline, not all older adults exhibit memory loss. Furthermore, emotional memories are not subject to the same extent of forgetting and appear preserved in aging. We conducted high-resolution fMRI during a task involving pattern separation of emotional information in older adults with and without age-related memory impairment (characterized by performance on a word-list learning task: low performers: LP vs. high performers: HP). We found signals consistent with emotional pattern separation in hippocampal dentate (DG)/CA3 in HP but not in LP individuals, suggesting a deficit in emotional pattern separation. During false recognition, we found increased DG/CA3 activity in LP individuals, suggesting that hyperactivity may be associated with overgeneralization. We additionally observed a selective deficit in basolateral amygdala-lateral entorhinal cortex-DG/CA3 functional connectivity in LP individuals during pattern separation of negative information. During negative false recognition, LP individuals showed increased medial temporal lobe functional connectivity, consistent with overgeneralization. Overall, these results suggest a novel mechanistic account of individual differences in emotional memory alterations exhibited in aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Age-related individual variability in memory performance is associated with amygdala-hippocampal circuit function and emotional pattern separation

PubMed Central

Leal, Stephanie L.; Noche, Jessica A.; Murray, Elizabeth A.; Yassa, Michael A.

2018-01-01

While aging is generally associated with episodic memory decline, not all older adults exhibit memory loss. Furthermore, emotional memories are not subject to the same extent of forgetting and appear preserved in aging. We conducted high-resolution fMRI during a task involving pattern separation of emotional information in older adults with and without age-related memory impairment (characterized by performance on a word-list learning task: low performers: LP vs. high performers: HP). We found signals consistent with emotional pattern separation in hippocampal dentate (DG)/CA3 in HP but not in LP individuals, suggesting a deficit in emotional pattern separation. During false recognition, we found increased DG/CA3 activity in LP individuals, suggesting that hyperactivity may be associated with overgeneralization. We additionally observed a selective deficit in basolateral amygdala—lateral entorhinal cortex—DG/CA3 functional connectivity in LP individuals during pattern separation of negative information. During negative false recognition, LP individuals showed increased medial temporal lobe functional connectivity, consistent with overgeneralization. Overall, these results suggest a novel mechanistic account of individual differences in emotional memory alterations exhibited in aging. PMID:27723500

Statistical assessment of speech system performance

NASA Technical Reports Server (NTRS)

Moshier, Stephen L.

1977-01-01

Methods for the normalization of performance tests results of speech recognition systems are presented. Technological accomplishments in speech recognition systems, as well as planned research activities are described.

A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements

PubMed Central

2014-01-01

Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditionally been limited to sequentially controlling DOFs one at a time. However, able-bodied individuals use multiple DOFs simultaneously, and it may be beneficial to provide amputees the ability to perform simultaneous movements. In this study, four amputees who had undergone targeted motor reinnervation (TMR) surgery with previous training using myoelectric prostheses were configured to use three control strategies: 1) conventional amplitude-based myoelectric control, 2) sequential (one-DOF) pattern recognition control, 3) simultaneous pattern recognition control. Simultaneous pattern recognition was enabled by having amputees train each simultaneous movement as a separate motion class. For tasks that required control over just one DOF, sequential pattern recognition based control performed the best with the lowest average completion times, completion rates and length error. For tasks that required control over 2 DOFs, the simultaneous pattern recognition controller performed the best with the lowest average completion times, completion rates and length error compared to the other control strategies. In the two strategies in which users could employ simultaneous movements (conventional and simultaneous pattern recognition), amputees chose to use simultaneous movements 78% of the time with simultaneous pattern recognition and 64% of the time with conventional control for tasks that required two DOF motions to reach the target. These results suggest that when amputees are given the ability to control multiple DOFs simultaneously, they choose to perform tasks that utilize multiple DOFs with simultaneous movements. Additionally, they were able to perform these tasks with higher performance (faster speed, lower length error and higher completion rates) without losing substantial performance in 1 DOF tasks. PMID:24410948

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Deletion of the GluA1 AMPA receptor subunit impairs recency-dependent object recognition memory

PubMed Central

Sanderson, David J.; Hindley, Emma; Smeaton, Emily; Denny, Nick; Taylor, Amy; Barkus, Chris; Sprengel, Rolf; Seeburg, Peter H.; Bannerman, David M.

2011-01-01

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes. PMID:21378100

Developmental change and cross-domain links in vocal and musical emotion recognition performance in childhood.

PubMed

Allgood, Rebecca; Heaton, Pamela

2015-09-01

Although the configurations of psychoacoustic cues signalling emotions in human vocalizations and instrumental music are very similar, cross-domain links in recognition performance have yet to be studied developmentally. Two hundred and twenty 5- to 10-year-old children were asked to identify musical excerpts and vocalizations as happy, sad, or fearful. The results revealed age-related increases in overall recognition performance with significant correlations across vocal and musical conditions at all developmental stages. Recognition scores were greater for musical than vocal stimuli and were superior in females compared with males. These results confirm that recognition of emotions in vocal and musical stimuli is linked by 5 years and that sensitivity to emotions in auditory stimuli is influenced by age and gender. © 2015 The British Psychological Society.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

A triboelectric motion sensor in wearable body sensor network for human activity recognition.

PubMed

Hui Huang; Xian Li; Ye Sun

2016-08-01

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

Selective visual attention and motivation: the consequences of value learning in an attentional blink task.

PubMed

Raymond, Jane E; O'Brien, Jennifer L

2009-08-01

Learning to associate the probability and value of behavioral outcomes with specific stimuli (value learning) is essential for rational decision making. However, in demanding cognitive conditions, access to learned values might be constrained by limited attentional capacity. We measured recognition of briefly presented faces seen previously in a value-learning task involving monetary wins and losses; the recognition task was performed both with and without constraints on available attention. Regardless of available attention, recognition was substantially enhanced for motivationally salient stimuli (i.e., stimuli highly predictive of outcomes), compared with equally familiar stimuli that had weak or no motivational salience, and this effect was found regardless of valence (win or loss). However, when attention was constrained (because stimuli were presented during an attentional blink, AB), valence determined recognition; win-associated faces showed no AB, but all other faces showed large ABs. Motivational salience acts independently of attention to modulate simple perceptual decisions, but when attention is limited, visual processing is biased in favor of reward-associated stimuli.

Autonomous learning in gesture recognition by using lobe component analysis

NASA Astrophysics Data System (ADS)

Lu, Jian; Weng, Juyang

2007-02-01

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Al-Rousan, M.

2005-12-01

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

NASA Astrophysics Data System (ADS)

Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

2003-12-01

Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

A bottom-up model of spatial attention predicts human error patterns in rapid scene recognition.

PubMed

Einhäuser, Wolfgang; Mundhenk, T Nathan; Baldi, Pierre; Koch, Christof; Itti, Laurent

2007-07-20

Humans demonstrate a peculiar ability to detect complex targets in rapidly presented natural scenes. Recent studies suggest that (nearly) no focal attention is required for overall performance in such tasks. Little is known, however, of how detection performance varies from trial to trial and which stages in the processing hierarchy limit performance: bottom-up visual processing (attentional selection and/or recognition) or top-down factors (e.g., decision-making, memory, or alertness fluctuations)? To investigate the relative contribution of these factors, eight human observers performed an animal detection task in natural scenes presented at 20 Hz. Trial-by-trial performance was highly consistent across observers, far exceeding the prediction of independent errors. This consistency demonstrates that performance is not primarily limited by idiosyncratic factors but by visual processing. Two statistical stimulus properties, contrast variation in the target image and the information-theoretical measure of "surprise" in adjacent images, predict performance on a trial-by-trial basis. These measures are tightly related to spatial attention, demonstrating that spatial attention and rapid target detection share common mechanisms. To isolate the causal contribution of the surprise measure, eight additional observers performed the animal detection task in sequences that were reordered versions of those all subjects had correctly recognized in the first experiment. Reordering increased surprise before and/or after the target while keeping the target and distractors themselves unchanged. Surprise enhancement impaired target detection in all observers. Consequently, and contrary to several previously published findings, our results demonstrate that attentional limitations, rather than target recognition alone, affect the detection of targets in rapidly presented visual sequences.

Some factors underlying individual differences in speech recognition on PRESTO: a first report.

PubMed

Tamati, Terrin N; Gilbert, Jaimie L; Pisoni, David B

2013-01-01

Previous studies investigating speech recognition in adverse listening conditions have found extensive variability among individual listeners. However, little is currently known about the core underlying factors that influence speech recognition abilities. To investigate sensory, perceptual, and neurocognitive differences between good and poor listeners on the Perceptually Robust English Sentence Test Open-set (PRESTO), a new high-variability sentence recognition test under adverse listening conditions. Participants who fell in the upper quartile (HiPRESTO listeners) or lower quartile (LoPRESTO listeners) on key word recognition on sentences from PRESTO in multitalker babble completed a battery of behavioral tasks and self-report questionnaires designed to investigate real-world hearing difficulties, indexical processing skills, and neurocognitive abilities. Young, normal-hearing adults (N = 40) from the Indiana University community participated in the current study. Participants' assessment of their own real-world hearing difficulties was measured with a self-report questionnaire on situational hearing and hearing health history. Indexical processing skills were assessed using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Neurocognitive abilities were measured with the Auditory Digit Span Forward (verbal short-term memory) and Digit Span Backward (verbal working memory) tests, the Stroop Color and Word Test (attention/inhibition), the WordFam word familiarity test (vocabulary size), the Behavioral Rating Inventory of Executive Function-Adult Version (BRIEF-A) self-report questionnaire on executive function, and two performance subtests of the Wechsler Abbreviated Scale of Intelligence (WASI) Performance Intelligence Quotient (IQ; nonverbal intelligence). Scores on self-report questionnaires and behavioral tasks were tallied and analyzed by listener group (HiPRESTO and LoPRESTO). The extreme groups did not differ overall on self-reported hearing difficulties in real-world listening environments. However, an item-by-item analysis of questions revealed that LoPRESTO listeners reported significantly greater difficulty understanding speakers in a public place. HiPRESTO listeners were significantly more accurate than LoPRESTO listeners at gender discrimination and regional dialect categorization, but they did not differ on talker discrimination accuracy or response time, or gender discrimination response time. HiPRESTO listeners also had longer forward and backward digit spans, higher word familiarity ratings on the WordFam test, and lower (better) scores for three individual items on the BRIEF-A questionnaire related to cognitive load. The two groups did not differ on the Stroop Color and Word Test or either of the WASI performance IQ subtests. HiPRESTO listeners and LoPRESTO listeners differed in indexical processing abilities, short-term and working memory capacity, vocabulary size, and some domains of executive functioning. These findings suggest that individual differences in the ability to encode and maintain highly detailed episodic information in speech may underlie the variability observed in speech recognition performance in adverse listening conditions using high-variability PRESTO sentences in multitalker babble. American Academy of Audiology.

Some Factors Underlying Individual Differences in Speech Recognition on PRESTO: A First Report

PubMed Central

Tamati, Terrin N.; Gilbert, Jaimie L.; Pisoni, David B.

2013-01-01

Background Previous studies investigating speech recognition in adverse listening conditions have found extensive variability among individual listeners. However, little is currently known about the core, underlying factors that influence speech recognition abilities. Purpose To investigate sensory, perceptual, and neurocognitive differences between good and poor listeners on PRESTO, a new high-variability sentence recognition test under adverse listening conditions. Research Design Participants who fell in the upper quartile (HiPRESTO listeners) or lower quartile (LoPRESTO listeners) on key word recognition on sentences from PRESTO in multitalker babble completed a battery of behavioral tasks and self-report questionnaires designed to investigate real-world hearing difficulties, indexical processing skills, and neurocognitive abilities. Study Sample Young, normal-hearing adults (N = 40) from the Indiana University community participated in the current study. Data Collection and Analysis Participants’ assessment of their own real-world hearing difficulties was measured with a self-report questionnaire on situational hearing and hearing health history. Indexical processing skills were assessed using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Neurocognitive abilities were measured with the Auditory Digit Span Forward (verbal short-term memory) and Digit Span Backward (verbal working memory) tests, the Stroop Color and Word Test (attention/inhibition), the WordFam word familiarity test (vocabulary size), the BRIEF-A self-report questionnaire on executive function, and two performance subtests of the WASI Performance IQ (non-verbal intelligence). Scores on self-report questionnaires and behavioral tasks were tallied and analyzed by listener group (HiPRESTO and LoPRESTO). Results The extreme groups did not differ overall on self-reported hearing difficulties in real-world listening environments. However, an item-by-item analysis of questions revealed that LoPRESTO listeners reported significantly greater difficulty understanding speakers in a public place. HiPRESTO listeners were significantly more accurate than LoPRESTO listeners at gender discrimination and regional dialect categorization, but they did not differ on talker discrimination accuracy or response time, or gender discrimination response time. HiPRESTO listeners also had longer forward and backward digit spans, higher word familiarity ratings on the WordFam test, and lower (better) scores for three individual items on the BRIEF-A questionnaire related to cognitive load. The two groups did not differ on the Stroop Color and Word Test or either of the WASI performance IQ subtests. Conclusions HiPRESTO listeners and LoPRESTO listeners differed in indexical processing abilities, short-term and working memory capacity, vocabulary size, and some domains of executive functioning. These findings suggest that individual differences in the ability to encode and maintain highly detailed episodic information in speech may underlie the variability observed in speech recognition performance in adverse listening conditions using high-variability PRESTO sentences in multitalker babble. PMID:24047949

Training and cockpit design to promote expert performance

NASA Technical Reports Server (NTRS)

Chappell, Sheryl L.

1991-01-01

The behavior of expert pilots in familiar situations is explored and the implications for better training programs and cockpit designs are stated. Experts in familiar operational situations performing highly practiced tasks are said to recognize and respond to complex situations using pattern recognition or intuition. For some tasks this class of behaviors is desirable; performance can be improved by reducing cognitive load and increasing speed and accuracy. Part-task training, training for monitoring and techniques for the transfer of knowledge can facilitate the development of these skills. Methods for promoting pattern recognition through pilot-aircraft interface design include the use of spatial presentations of information and providing triggering events. In some instances, the familiar, well-practiced behavior is not appropriate and it is desirable to prevent the response. When prevention is necessary, barriers can be constructed in the interface to remind the pilot of the inappropriateness of the response.

Family environment influences emotion recognition following paediatric traumatic brain injury.

PubMed

Schmidt, Adam T; Orsten, Kimberley D; Hanten, Gerri R; Li, Xiaoqi; Levin, Harvey S

2010-01-01

This study investigated the relationship between family functioning and performance on two tasks of emotion recognition (emotional prosody and face emotion recognition) and a cognitive control procedure (the Flanker task) following paediatric traumatic brain injury (TBI) or orthopaedic injury (OI). A total of 142 children (75 TBI, 67 OI) were assessed on three occasions: baseline, 3 months and 1 year post-injury on the two emotion recognition tasks and the Flanker task. Caregivers also completed the Life Stressors and Resources Scale (LISRES) on each occasion. Growth curve analysis was used to analyse the data. Results indicated that family functioning influenced performance on the emotional prosody and Flanker tasks but not on the face emotion recognition task. Findings on both the emotional prosody and Flanker tasks were generally similar across groups. However, financial resources emerged as significantly related to emotional prosody performance in the TBI group only (p = 0.0123). Findings suggest family functioning variables--especially financial resources--can influence performance on an emotional processing task following TBI in children.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Super-recognizers: People with extraordinary face recognition ability

PubMed Central

Russell, Richard; Duchaine, Brad; Nakayama, Ken

2014-01-01

We tested four people who claimed to have significantly better than ordinary face recognition ability. Exceptional ability was confirmed in each case. On two very different tests of face recognition, all four experimental subjects performed beyond the range of control subject performance. They also scored significantly better than average on a perceptual discrimination test with faces. This effect was larger with upright than inverted faces, and the four subjects showed a larger ‘inversion effect’ than control subjects, who in turn showed a larger inversion effect than developmental prosopagnosics. This indicates an association between face recognition ability and the magnitude of the inversion effect. Overall, these ‘super-recognizers’ are about as good at face recognition and perception as developmental prosopagnosics are bad. Our findings demonstrate the existence of people with exceptionally good face recognition ability, and show that the range of face recognition and face perception ability is wider than previously acknowledged. PMID:19293090

Should visual speech cues (speechreading) be considered when fitting hearing aids?

NASA Astrophysics Data System (ADS)

Grant, Ken

2002-05-01

When talker and listener are face-to-face, visual speech cues become an important part of the communication environment, and yet, these cues are seldom considered when designing hearing aids. Models of auditory-visual speech recognition highlight the importance of complementary versus redundant speech information for predicting auditory-visual recognition performance. Thus, for hearing aids to work optimally when visual speech cues are present, it is important to know whether the cues provided by amplification and the cues provided by speechreading complement each other. In this talk, data will be reviewed that show nonmonotonicity between auditory-alone speech recognition and auditory-visual speech recognition, suggesting that efforts designed solely to improve auditory-alone recognition may not always result in improved auditory-visual recognition. Data will also be presented showing that one of the most important speech cues for enhancing auditory-visual speech recognition performance, voicing, is often the cue that benefits least from amplification.

Super-recognizers: people with extraordinary face recognition ability.

PubMed

Russell, Richard; Duchaine, Brad; Nakayama, Ken

2009-04-01

We tested 4 people who claimed to have significantly better than ordinary face recognition ability. Exceptional ability was confirmed in each case. On two very different tests of face recognition, all 4 experimental subjects performed beyond the range of control subject performance. They also scored significantly better than average on a perceptual discrimination test with faces. This effect was larger with upright than with inverted faces, and the 4 subjects showed a larger "inversion effect" than did control subjects, who in turn showed a larger inversion effect than did developmental prosopagnosics. This result indicates an association between face recognition ability and the magnitude of the inversion effect. Overall, these "super-recognizers" are about as good at face recognition and perception as developmental prosopagnosics are bad. Our findings demonstrate the existence of people with exceptionally good face recognition ability and show that the range of face recognition and face perception ability is wider than has been previously acknowledged.

Mechanism of molecular recognition on molecular imprinted monolith by capillary electrochromatography.

PubMed

Liu, Zhao-Sheng; Xu, Yan-Li; Yan, Chao; Gao, Ru-Yu

2005-09-16

The recognition mechanism of molecularly imprinted polymer (MIP) in capillary electrochromatography (CEC) is complicated since it possesses a hybrid process, which comprises the features of chromatographic retention, electrophoretic migration and molecular imprinting. For an understanding of the molecular recognition of MIP in CEC, a monolithic MIP in a capillary with 1,1'-binaphthyl-2,2'-diamine (BNA) imprinting was prepared by in situ copolymerization of imprinted molecule, methacrylic acid and ethylene glycol dimethacrylate in porogenic solvent, a mixture of toluene-isooctane. Strong recognition ability and high column performance (theory plates was 43,000 plates/m) of BNA were achieved on this monolithic MIP in CEC mode. In addition, BNA and its structural analogue, 1,1'-bi-2, 2'-naphthol, differing in functional groups, were used as model compounds to study imprinting effect on the resultant BNA-imprinted monolithic column, a reference column without imprinting of BNA and a open capillary. The effects of organic modifier concentration, pH value of buffer, salt concentration of buffer and column temperature on the retention and recognition of two compounds were investigated. The results showed that the molecular recognition on MIP monolith in CEC mode mainly derived from imprinting cavities on BNA-imprinted polymer other than chromatographic retention and electrophoretic migration.

Extrinsic Cognitive Load Impairs Spoken Word Recognition in High- and Low-Predictability Sentences.

PubMed

Hunter, Cynthia R; Pisoni, David B

Listening effort (LE) induced by speech degradation reduces performance on concurrent cognitive tasks. However, a converse effect of extrinsic cognitive load on recognition of spoken words in sentences has not been shown. The aims of the present study were to (a) examine the impact of extrinsic cognitive load on spoken word recognition in a sentence recognition task and (b) determine whether cognitive load and/or LE needed to understand spectrally degraded speech would differentially affect word recognition in high- and low-predictability sentences. Downstream effects of speech degradation and sentence predictability on the cognitive load task were also examined. One hundred twenty young adults identified sentence-final spoken words in high- and low-predictability Speech Perception in Noise sentences. Cognitive load consisted of a preload of short (low-load) or long (high-load) sequences of digits, presented visually before each spoken sentence and reported either before or after identification of the sentence-final word. LE was varied by spectrally degrading sentences with four-, six-, or eight-channel noise vocoding. Level of spectral degradation and order of report (digits first or words first) were between-participants variables. Effects of cognitive load, sentence predictability, and speech degradation on accuracy of sentence-final word identification as well as recall of preload digit sequences were examined. In addition to anticipated main effects of sentence predictability and spectral degradation on word recognition, we found an effect of cognitive load, such that words were identified more accurately under low load than high load. However, load differentially affected word identification in high- and low-predictability sentences depending on the level of sentence degradation. Under severe spectral degradation (four-channel vocoding), the effect of cognitive load on word identification was present for high-predictability sentences but not for low-predictability sentences. Under mild spectral degradation (eight-channel vocoding), the effect of load was present for low-predictability sentences but not for high-predictability sentences. There were also reliable downstream effects of speech degradation and sentence predictability on recall of the preload digit sequences. Long digit sequences were more easily recalled following spoken sentences that were less spectrally degraded. When digits were reported after identification of sentence-final words, short digit sequences were recalled more accurately when the spoken sentences were predictable. Extrinsic cognitive load can impair recognition of spectrally degraded spoken words in a sentence recognition task. Cognitive load affected word identification in both high- and low-predictability sentences, suggesting that load may impact both context use and lower-level perceptual processes. Consistent with prior work, LE also had downstream effects on memory for visual digit sequences. Results support the proposal that extrinsic cognitive load and LE induced by signal degradation both draw on a central, limited pool of cognitive resources that is used to recognize spoken words in sentences under adverse listening conditions.

Using eye movements as an index of implicit face recognition in autism spectrum disorder.

PubMed

Hedley, Darren; Young, Robyn; Brewer, Neil

2012-10-01

Individuals with an autism spectrum disorder (ASD) typically show impairment on face recognition tasks. Performance has usually been assessed using overt, explicit recognition tasks. Here, a complementary method involving eye tracking was used to examine implicit face recognition in participants with ASD and in an intelligence quotient-matched non-ASD control group. Differences in eye movement indices between target and foil faces were used as an indicator of implicit face recognition. Explicit face recognition was assessed using old-new discrimination and reaction time measures. Stimuli were faces of studied (target) or unfamiliar (foil) persons. Target images at test were either identical to the images presented at study or altered by changing the lighting, pose, or by masking with visual noise. Participants with ASD performed worse than controls on the explicit recognition task. Eye movement-based measures, however, indicated that implicit recognition may not be affected to the same degree as explicit recognition. Autism Res 2012, 5: 363-379. © 2012 International Society for Autism Research, Wiley Periodicals, Inc. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

Exploring a Decrease in Recognition Performance for Non-Antecedents Following the Processing of Anaphors

ERIC Educational Resources Information Center

Dopkins, Stephen; Nordlie, Johanna

2011-01-01

Recognition judgments to the non-antecedents of a repeated-noun anaphor are slower and less accurate after than before the processing of the anaphor. Disagreement exists as to whether this pattern of performance reflects a bias shift carried out by a memory process associated with the recognition of a word that has previously occurred in the…

Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model.

PubMed

Jürgens, Tim; Brand, Thomas

2009-11-01

This study compares the phoneme recognition performance in speech-shaped noise of a microscopic model for speech recognition with the performance of normal-hearing listeners. "Microscopic" is defined in terms of this model twofold. First, the speech recognition rate is predicted on a phoneme-by-phoneme basis. Second, microscopic modeling means that the signal waveforms to be recognized are processed by mimicking elementary parts of human's auditory processing. The model is based on an approach by Holube and Kollmeier [J. Acoust. Soc. Am. 100, 1703-1716 (1996)] and consists of a psychoacoustically and physiologically motivated preprocessing and a simple dynamic-time-warp speech recognizer. The model is evaluated while presenting nonsense speech in a closed-set paradigm. Averaged phoneme recognition rates, specific phoneme recognition rates, and phoneme confusions are analyzed. The influence of different perceptual distance measures and of the model's a-priori knowledge is investigated. The results show that human performance can be predicted by this model using an optimal detector, i.e., identical speech waveforms for both training of the recognizer and testing. The best model performance is yielded by distance measures which focus mainly on small perceptual distances and neglect outliers.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Association of impaired facial affect recognition with basic facial and visual processing deficits in schizophrenia.

PubMed

Norton, Daniel; McBain, Ryan; Holt, Daphne J; Ongur, Dost; Chen, Yue

2009-06-15

Impaired emotion recognition has been reported in schizophrenia, yet the nature of this impairment is not completely understood. Recognition of facial emotion depends on processing affective and nonaffective facial signals, as well as basic visual attributes. We examined whether and how poor facial emotion recognition in schizophrenia is related to basic visual processing and nonaffective face recognition. Schizophrenia patients (n = 32) and healthy control subjects (n = 29) performed emotion discrimination, identity discrimination, and visual contrast detection tasks, where the emotionality, distinctiveness of identity, or visual contrast was systematically manipulated. Subjects determined which of two presentations in a trial contained the target: the emotional face for emotion discrimination, a specific individual for identity discrimination, and a sinusoidal grating for contrast detection. Patients had significantly higher thresholds (worse performance) than control subjects for discriminating both fearful and happy faces. Furthermore, patients' poor performance in fear discrimination was predicted by performance in visual detection and face identity discrimination. Schizophrenia patients require greater emotional signal strength to discriminate fearful or happy face images from neutral ones. Deficient emotion recognition in schizophrenia does not appear to be determined solely by affective processing but is also linked to the processing of basic visual and facial information.

Preserved recognition in a case of developmental amnesia: implications for the acquisition of semantic memory?

PubMed

Baddeley, A; Vargha-Khadem, F; Mishkin, M

2001-04-01

We report the performance on recognition memory tests of Jon, who, despite amnesia from early childhood, has developed normal levels of performance on tests of intelligence, language, and general knowledge. Despite impaired recall, he performed within the normal range on each of six recognition tests, but he appears to lack the recollective phenomenological experience normally associated with episodic memory. His recall of previously unfamiliar newsreel events was impaired, but gained substantially from repetition over a 2-day period. Our results are consistent with the hypothesis that the recollective process of episodic memory is not necessary either for recognition or for the acquisition of semantic knowledge.

Real-Time Hand Posture Recognition Using a Range Camera

NASA Astrophysics Data System (ADS)

Lahamy, Herve

The basic goal of human computer interaction is to improve the interaction between users and computers by making computers more usable and receptive to the user's needs. Within this context, the use of hand postures in replacement of traditional devices such as keyboards, mice and joysticks is being explored by many researchers. The goal is to interpret human postures via mathematical algorithms. Hand posture recognition has gained popularity in recent years, and could become the future tool for humans to interact with computers or virtual environments. An exhaustive description of the frequently used methods available in literature for hand posture recognition is provided. It focuses on the different types of sensors and data used, the segmentation and tracking methods, the features used to represent the hand postures as well as the classifiers considered in the recognition process. Those methods are usually presented as highly robust with a recognition rate close to 100%. However, a couple of critical points necessary for a successful real-time hand posture recognition system require major improvement. Those points include the features used to represent the hand segment, the number of postures simultaneously recognizable, the invariance of the features with respect to rotation, translation and scale and also the behavior of the classifiers against non-perfect hand segments for example segments including part of the arm or missing part of the palm. A 3D time-of-flight camera named SR4000 has been chosen to develop a new methodology because of its capability to provide in real-time and at high frame rate 3D information on the scene imaged. This sensor has been described and evaluated for its capability for capturing in real-time a moving hand. A new recognition method that uses the 3D information provided by the range camera to recognize hand postures has been proposed. The different steps of this methodology including the segmentation, the tracking, the hand modeling and finally the recognition process have been described and evaluated extensively. In addition, the performance of this method has been analyzed against several existing hand posture recognition techniques found in literature. The proposed system is able to recognize with an overall recognition rate of 98% and in real-time 18 out the 33 postures of the American sign language alphabet. This recognition is translation, rotation and scale invariant.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

A frontier analysis approach for benchmarking hospital performance in the treatment of acute myocardial infarction.

PubMed

Stanford, Robert E

2004-05-01

This paper uses a non-parametric frontier model and adaptations of the concepts of cross-efficiency and peer-appraisal to develop a formal methodology for benchmarking provider performance in the treatment of Acute Myocardial Infarction (AMI). Parameters used in the benchmarking process are the rates of proper recognition of indications of six standard treatment processes for AMI; the decision making units (DMUs) to be compared are the Medicare eligible hospitals of a particular state; the analysis produces an ordinal ranking of individual hospital performance scores. The cross-efficiency/peer-appraisal calculation process is constructed to accommodate DMUs that experience no patients in some of the treatment categories. While continuing to rate highly the performances of DMUs which are efficient in the Pareto-optimal sense, our model produces individual DMU performance scores that correlate significantly with good overall performance, as determined by a comparison of the sums of the individual DMU recognition rates for the six standard treatment processes. The methodology is applied to data collected from 107 state Medicare hospitals.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

Dyslexia in general practice education: considerations for recognition and support.

PubMed

Shrewsbury, Duncan

2016-07-01

Dyslexia is a common developmental learning difficulty, which persists throughout life. It is highly likely that those working in primary care will know, or even work with someone who has dyslexia. Dyslexia can impact on performance in postgraduate training and exams. The stereotypical characteristics of dyslexia, such as literacy difficulties, are often not obvious in adult learners. Instead, recognition requires a holistic approach to evaluating personal strengths and difficulties, in the context of a supportive relationship. Strategies to support dyslexic learners should consider recommendations made in formal diagnostic reports, and aim to address self-awareness and coping skills.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

Learning effect of computerized cognitive tests in older adults

PubMed Central

de Oliveira, Rafaela Sanches; Trezza, Beatriz Maria; Busse, Alexandre Leopold; Jacob-Filho, Wilson

2014-01-01

ABSTRACT Objective: To evaluate the learning effect of computerized cognitive testing in the elderly. Methods: Cross-sectional study with 20 elderly, 10 women and 10 men, with average age of 77.5 (±4.28) years. The volunteers performed two series of computerized cognitive tests in sequence and their results were compared. The applied tests were: Trail Making A and B, Spatial Recognition, Go/No Go, Memory Span, Pattern Recognition Memory and Reverse Span. Results: Based on the comparison of the results, learning effects were observed only in the Trail Making A test (p=0.019). Other tests performed presented no significant performance improvements. There was no correlation between learning effect and age (p=0.337) and education (p=0.362), as well as differences between genders (p=0.465). Conclusion: The computerized cognitive tests repeated immediately afterwards, for elderly, revealed no change in their performance, with the exception of the Trail Making test, demonstrating high clinical applicability, even in short intervals. PMID:25003917

The role of the thalamic nuclei in recognition memory accompanied by recall during encoding and retrieval: an fMRI study.

PubMed

Pergola, Giulio; Ranft, Alexander; Mathias, Klaus; Suchan, Boris

2013-07-01

The present functional imaging study aimed at investigating the contribution of the mediodorsal nucleus and the anterior nuclei of the thalamus with their related cortical networks to recognition memory and recall. Eighteen subjects performed associative picture encoding followed by a single item recognition test during the functional magnetic resonance imaging session. After scanning, subjects performed a cued recall test using the formerly recognized pictures as cues. This post-scanning test served to classify recognition trials according to subsequent recall performance. In general, single item recognition accompanied by successful recall of the associations elicited stronger activation in the mediodorsal nucleus of the thalamus and in the prefrontal cortices both during encoding and retrieval compared to recognition without recall. In contrast, the anterior nuclei of the thalamus were selectively active during the retrieval phase of recognition followed by recall. A correlational analysis showed that activation of the anterior thalamus during retrieval as assessed by measuring the percent signal changes predicted lower rates of recognition without recall. These findings show that the thalamus is critical for recognition accompanied by recall, and provide the first evidence of a functional segregation of the thalamic nuclei with respect to the memory retrieval phase. In particular, the mediodorsal thalamic-prefrontal cortical network is activated during successful encoding and retrieval of associations, which suggests a role of this system in recall and recollection. The activity of the anterior thalamic-temporal network selectively during retrieval predicts better memory performances across subjects and this confirms the paramount role of this network in recall and recollection. Copyright © 2013 Elsevier Inc. All rights reserved.

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Storage and retrieval properties of dual codes for pictures and words in recognition memory.

PubMed

Snodgrass, J G; McClure, P

1975-09-01

Storage and retrieval properties of pictures and words were studied within a recognition memory paradigm. Storage was manipulated by instructing subjects either to image or to verbalize to both picture and word stimuli during the study sequence. Retrieval was manipulated by representing a proportion of the old picture and word items in their opposite form during the recognition test (i.e., some old pictures were tested with their corresponding words and vice versa). Recognition performance for pictures was identical under the two instructional conditions, whereas recognition performance for words was markedly superior under the imagery instruction condition. It was suggested that subjects may engage in dual coding of simple pictures naturally, regardless of instructions, whereas dual coding of words may occur only under imagery instructions. The form of the test item had no effect on recognition performance for either type of stimulus and under either instructional condition. However, change of form of the test item markedly reduced item-by-item correlations between the two instructional conditions. It is tentatively proposed that retrieval is required in recognition, but that the effect of a form change is simply to make the retrieval process less consistent, not less efficient.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

Facial emotion recognition in patients with focal and diffuse axonal injury.

PubMed

Yassin, Walid; Callahan, Brandy L; Ubukata, Shiho; Sugihara, Genichi; Murai, Toshiya; Ueda, Keita

2017-01-01

Facial emotion recognition impairment has been well documented in patients with traumatic brain injury. Studies exploring the neural substrates involved in such deficits have implicated specific grey matter structures (e.g. orbitofrontal regions), as well as diffuse white matter damage. Our study aims to clarify whether different types of injuries (i.e. focal vs. diffuse) will lead to different types of impairments on facial emotion recognition tasks, as no study has directly compared these patients. The present study examined performance and response patterns on a facial emotion recognition task in 14 participants with diffuse axonal injury (DAI), 14 with focal injury (FI) and 22 healthy controls. We found that, overall, participants with FI and DAI performed more poorly than controls on the facial emotion recognition task. Further, we observed comparable emotion recognition performance in participants with FI and DAI, despite differences in the nature and distribution of their lesions. However, the rating response pattern between the patient groups was different. This is the first study to show that pure DAI, without gross focal lesions, can independently lead to facial emotion recognition deficits and that rating patterns differ depending on the type and location of trauma.

Blueberry supplementation improves memory in middle-aged mice fed a high-fat diet.

PubMed

Carey, Amanda N; Gomes, Stacey M; Shukitt-Hale, Barbara

2014-05-07

Consuming a high-fat diet may result in behavioral deficits similar to those observed in aging animals. It has been demonstrated that blueberry supplementation can allay age-related behavioral deficits. To determine if supplementation of a high-fat diet with blueberries offers protection against putative high-fat diet-related declines, 9-month-old C57Bl/6 mice were maintained on low-fat (10% fat calories) or high-fat (60% fat calories) diets with and without 4% freeze-dried blueberry powder. Novel object recognition memory was impaired by the high-fat diet; after 4 months on the high-fat diet, mice spent 50% of their time on the novel object in the testing trial, performing no greater than chance performance. Blueberry supplementation prevented recognition memory deficits after 4 months on the diets, as mice on this diet spent 67% of their time on the novel object. After 5 months on the diets, mice consuming the high-fat diet passed through the platform location less often than mice on low-fat diets during probe trials on days 2 and 3 of Morris water maze testing, whereas mice consuming the high-fat blueberry diet passed through the platform location as often as mice on the low-fat diets. This study is a first step in determining if incorporating more nutrient-dense foods into a high-fat diet can allay cognitive dysfunction.

Studies of recognition with multitemporal remote sensor data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Cicone, R. C.

1975-01-01

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Dynamic facial expression recognition based on geometric and texture features

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Zengfu

2018-04-01

Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Information-based approach to performance estimation and requirements allocation in multisensor fusion for target recognition

NASA Astrophysics Data System (ADS)

Harney, Robert C.

1997-03-01

A novel methodology offering the potential for resolving two of the significant problems of implementing multisensor target recognition systems, i.e., the rational selection of a specific sensor suite and optimal allocation of requirements among sensors, is presented. Based on a sequence of conjectures (and their supporting arguments) concerning the relationship of extractable information content to recognition performance of a sensor system, a set of heuristics (essentially a reformulation of Johnson's criteria applicable to all sensor and data types) is developed. An approach to quantifying the information content of sensor data is described. Coupling this approach with the widely accepted Johnson's criteria for target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, nonimagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. The application of the technique to sensor selection and requirements allocation is discussed.

Students' Attitudes toward High-Stakes Testing and Its Effect on Educational Decisions

ERIC Educational Resources Information Center

Moran, Aldo Alfredo

2010-01-01

With the recent increase in accountability due to No Child Left Behind, graduation rates and drop-out rates are important indicators of how well a school district is performing. High-stakes testing scores are at the forefront of a school's success and recognition as a school that is preparing and graduating students to meet society's challenging…

Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes

PubMed Central

Piekarczyk, Marcin; Ogiela, Marek R.

2017-01-01

The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2–4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, 100% actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was 94.2%, which is a very good result for this type of complex action. PMID:29125560

Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia.

PubMed

Oh, Jooyoung; Chun, Ji-Won; Kim, Eunseong; Park, Hae-Jeong; Lee, Boreom; Kim, Jae-Jin

2017-01-01

Patients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old-new recognition task. Sixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme-identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old-new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old-new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed. The patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients. The functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Face engagement during infancy predicts later face recognition ability in younger siblings of children with autism.

PubMed

de Klerk, Carina C J M; Gliga, Teodora; Charman, Tony; Johnson, Mark H

2014-07-01

Face recognition difficulties are frequently documented in children with autism spectrum disorders (ASD). It has been hypothesized that these difficulties result from a reduced interest in faces early in life, leading to decreased cortical specialization and atypical development of the neural circuitry for face processing. However, a recent study by our lab demonstrated that infants at increased familial risk for ASD, irrespective of their diagnostic status at 3 years, exhibit a clear orienting response to faces. The present study was conducted as a follow-up on the same cohort to investigate how measures of early engagement with faces relate to face-processing abilities later in life. We also investigated whether face recognition difficulties are specifically related to an ASD diagnosis, or whether they are present at a higher rate in all those at familial risk. At 3 years we found a reduced ability to recognize unfamiliar faces in the high-risk group that was not specific to those children who received an ASD diagnosis, consistent with face recognition difficulties being an endophenotype of the disorder. Furthermore, we found that longer looking at faces at 7 months was associated with poorer performance on the face recognition task at 3 years in the high-risk group. These findings suggest that longer looking at faces in infants at risk for ASD might reflect early face-processing difficulties and predicts difficulties with recognizing faces later in life. © 2013 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

PubMed Central

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints. PMID:27579033

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.

PubMed

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng; Kuo, Chung-Hsien

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

Accelerometer's position independent physical activity recognition system for long-term activity monitoring in the elderly.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung; Kim, Tae-Seong

2010-12-01

Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subject's body, even for the same activity, resulting in high within-class variance. Existing accelerometer-based activity recognition systems thus require firm attachment of the sensor to a subject's body. This requirement makes them impractical for long-term activity monitoring during unsupervised free-living as it forces subjects into a fixed life pattern and impede their daily activities. Therefore, we introduce a novel single-triaxial-accelerometer-based activity recognition system that reduces the high within-class variance significantly and allows subjects to carry the sensor freely in any pocket without its firm attachment. We validated our system using seven activities: resting (lying/sitting/standing), walking, walking-upstairs, walking-downstairs, running, cycling, and vacuuming, recorded from five positions: chest pocket, front left trousers pocket, front right trousers pocket, rear trousers pocket, and inner jacket pocket. Its simplicity, ability to perform activities unimpeded, and an average recognition accuracy of 94% make our system a practical solution for continuous long-term activity monitoring in the elderly.

Processing of Acoustic Cues in Lexical-Tone Identification by Pediatric Cochlear-Implant Recipients

PubMed Central

Peng, Shu-Chen; Lu, Hui-Ping; Lu, Nelson; Lin, Yung-Song; Deroche, Mickael L. D.

2017-01-01

Purpose The objective was to investigate acoustic cue processing in lexical-tone recognition by pediatric cochlear-implant (CI) recipients who are native Mandarin speakers. Method Lexical-tone recognition was assessed in pediatric CI recipients and listeners with normal hearing (NH) in 2 tasks. In Task 1, participants identified naturally uttered words that were contrastive in lexical tones. For Task 2, a disyllabic word (yanjing) was manipulated orthogonally, varying in fundamental-frequency (F0) contours and duration patterns. Participants identified each token with the second syllable jing pronounced with Tone 1 (a high level tone) as eyes or with Tone 4 (a high falling tone) as eyeglasses. Results CI participants' recognition accuracy was significantly lower than NH listeners' in Task 1. In Task 2, CI participants' reliance on F0 contours was significantly less than that of NH listeners; their reliance on duration patterns, however, was significantly higher than that of NH listeners. Both CI and NH listeners' performance in Task 1 was significantly correlated with their reliance on F0 contours in Task 2. Conclusion For pediatric CI recipients, lexical-tone recognition using naturally uttered words is primarily related to their reliance on F0 contours, although duration patterns may be used as an additional cue. PMID:28388709

National Athletic Trainers' Association Position Statement: Exertional Heat Illnesses.

PubMed

Casa, Douglas J; DeMartini, Julie K; Bergeron, Michael F; Csillan, Dave; Eichner, E Randy; Lopez, Rebecca M; Ferrara, Michael S; Miller, Kevin C; O'Connor, Francis; Sawka, Michael N; Yeargin, Susan W

2015-08-18

 To present best-practice recommendations for the prevention, recognition, and treatment of exertional heat illnesses (EHIs) and to describe the relevant physiology of thermoregulation.  Certified athletic trainers recognize and treat athletes with EHIs, often in high-risk environments. Although the proper recognition and successful treatment strategies are well documented, EHIs continue to plague athletes, and exertional heat stroke remains one of the leading causes of sudden death during sport. The recommendations presented in this document provide athletic trainers and allied health providers with an integrated scientific and clinically applicable approach to the prevention, recognition, treatment, and return-to-activity guidelines for EHIs. These recommendations are given so that proper recognition and treatment can be accomplished in order to maximize the safety and performance of athletes.  Athletic trainers and other allied health care professionals should use these recommendations to establish onsite emergency action plans for their venues and athletes. The primary goal of athlete safety is addressed through the appropriate prevention strategies, proper recognition tactics, and effective treatment plans for EHIs. Athletic trainers and other allied health care professionals must be properly educated and prepared to respond in an expedient manner to alleviate symptoms and minimize the morbidity and mortality associated with these illnesses.

National Athletic Trainers' Association Position Statement: Exertional Heat Illnesses.

PubMed

Casa, Douglas J; DeMartini, Julie K; Bergeron, Michael F; Csillan, Dave; Eichner, E Randy; Lopez, Rebecca M; Ferrara, Michael S; Miller, Kevin C; O'Connor, Francis; Sawka, Michael N; Yeargin, Susan W

2015-09-01

To present best-practice recommendations for the prevention, recognition, and treatment of exertional heat illnesses (EHIs) and to describe the relevant physiology of thermoregulation. Certified athletic trainers recognize and treat athletes with EHIs, often in high-risk environments. Although the proper recognition and successful treatment strategies are well documented, EHIs continue to plague athletes, and exertional heat stroke remains one of the leading causes of sudden death during sport. The recommendations presented in this document provide athletic trainers and allied health providers with an integrated scientific and clinically applicable approach to the prevention, recognition, treatment of, and return-to-activity guidelines for EHIs. These recommendations are given so that proper recognition and treatment can be accomplished in order to maximize the safety and performance of athletes. Athletic trainers and other allied health care professionals should use these recommendations to establish onsite emergency action plans for their venues and athletes. The primary goal of athlete safety is addressed through the appropriate prevention strategies, proper recognition tactics, and effective treatment plans for EHIs. Athletic trainers and other allied health care professionals must be properly educated and prepared to respond in an expedient manner to alleviate symptoms and minimize the morbidity and mortality associated with these illnesses.

A cross-syndrome study of the development of holistic face recognition in children with autism, Down syndrome, and Williams syndrome.

PubMed

Annaz, Dagmara; Karmiloff-Smith, Annette; Johnson, Mark H; Thomas, Michael S C

2009-04-01

We report a cross-syndrome comparison of the development of holistic processing in face recognition in school-aged children with developmental disorders: autism, Down syndrome, and Williams syndrome. The autism group was split into two groups: one with high-functioning children and one with low-functioning children. The latter group has rarely been studied in this context. The four disorder groups were compared with typically developing children. Cross-sectional trajectory analyses were used to compare development in a modified version of Tanaka and Farah's part-whole task. Trajectories were constructed linking part-whole performance either to chronological age or to several measures of mental age (receptive vocabulary, visuospatial construction, and the Benton Facial Recognition Test). In addition to variable delays in onset and rate of development, we found an atypical profile in all disorder groups. These profiles were atypical in different ways, indicating multiple pathways to, and variable outcomes in, the development of face recognition. We discuss the implications for theories of face recognition in both atypical and typical development, including the idea that part-whole and rotation manipulations may tap different aspects of holistic and/or configural processing.

When the face fits: recognition of celebrities from matching and mismatching faces and voices.

PubMed

Stevenage, Sarah V; Neil, Greg J; Hamlin, Iain

2014-01-01

The results of two experiments are presented in which participants engaged in a face-recognition or a voice-recognition task. The stimuli were face-voice pairs in which the face and voice were co-presented and were either "matched" (same person), "related" (two highly associated people), or "mismatched" (two unrelated people). Analysis in both experiments confirmed that accuracy and confidence in face recognition was consistently high regardless of the identity of the accompanying voice. However accuracy of voice recognition was increasingly affected as the relationship between voice and accompanying face declined. Moreover, when considering self-reported confidence in voice recognition, confidence remained high for correct responses despite the proportion of these responses declining across conditions. These results converged with existing evidence indicating the vulnerability of voice recognition as a relatively weak signaller of identity, and results are discussed in the context of a person-recognition framework.

Visual novel stimuli in an ERP novelty oddball paradigm: effects of familiarity on repetition and recognition memory.

PubMed

Cycowicz, Yael M; Friedman, David

2007-01-01

The orienting response, the brain's reaction to novel and/or out of context familiar events, is reflected by the novelty P3 of the ERP. Contextually novel events also engender high rates of recognition memory. We examined, under incidental and intentional conditions, the effects of visual symbol familiarity on the novelty P3 recorded during an oddball task and on the parietal episodic memory (EM) effect, an index of recollection. Repetition of familiar, but not unfamiliar, symbols elicited a reduction in the novelty P3. Better recognition performance for the familiar symbols was associated with a robust parietal EM effect, which was absent for the unfamiliar symbols in the incidental task. These data demonstrate that processing of novel events depends on expectation and whether stimuli have preexisting representations in long-term semantic memory.

Visual face-movement sensitive cortex is relevant for auditory-only speech recognition.

PubMed

Riedel, Philipp; Ragert, Patrick; Schelinski, Stefanie; Kiebel, Stefan J; von Kriegstein, Katharina

2015-07-01

It is commonly assumed that the recruitment of visual areas during audition is not relevant for performing auditory tasks ('auditory-only view'). According to an alternative view, however, the recruitment of visual cortices is thought to optimize auditory-only task performance ('auditory-visual view'). This alternative view is based on functional magnetic resonance imaging (fMRI) studies. These studies have shown, for example, that even if there is only auditory input available, face-movement sensitive areas within the posterior superior temporal sulcus (pSTS) are involved in understanding what is said (auditory-only speech recognition). This is particularly the case when speakers are known audio-visually, that is, after brief voice-face learning. Here we tested whether the left pSTS involvement is causally related to performance in auditory-only speech recognition when speakers are known by face. To test this hypothesis, we applied cathodal transcranial direct current stimulation (tDCS) to the pSTS during (i) visual-only speech recognition of a speaker known only visually to participants and (ii) auditory-only speech recognition of speakers they learned by voice and face. We defined the cathode as active electrode to down-regulate cortical excitability by hyperpolarization of neurons. tDCS to the pSTS interfered with visual-only speech recognition performance compared to a control group without pSTS stimulation (tDCS to BA6/44 or sham). Critically, compared to controls, pSTS stimulation additionally decreased auditory-only speech recognition performance selectively for voice-face learned speakers. These results are important in two ways. First, they provide direct evidence that the pSTS is causally involved in visual-only speech recognition; this confirms a long-standing prediction of current face-processing models. Secondly, they show that visual face-sensitive pSTS is causally involved in optimizing auditory-only speech recognition. These results are in line with the 'auditory-visual view' of auditory speech perception, which assumes that auditory speech recognition is optimized by using predictions from previously encoded speaker-specific audio-visual internal models. Copyright © 2015 Elsevier Ltd. All rights reserved.

The relationship between social cognition and executive function in Major Depressive Disorder in high-functioning adolescents and young adults.

PubMed

Förster, Katharina; Jörgens, Silke; Air, Tracy M; Bürger, Christian; Enneking, Verena; Redlich, Ronny; Zaremba, Dario; Grotegerd, Dominik; Dohm, Katharina; Meinert, Susanne; Leehr, Elisabeth J; Böhnlein, Joscha; Repple, Jonathan; Opel, Nils; Kavakbasi, Erhan; Arolt, Volker; Zwitserlood, Pienie; Dannlowski, Udo; Baune, Bernhard T

2018-05-01

To understand how cognitive dysfunction contributes to social cognitive deficits in depression, we investigated the relationship between executive function and social cognitive performance in adolescents and young adults during current and remitted depression, compared to healthy controls. Social cognition and executive function were measured in 179 students (61 healthy controls and 118 patients with depression; M age = 20.60 years; SD age = 3.82 years). Hierarchical regression models were employed within each group (healthy controls, remitted depression, current depression) to examine the nature of associations between cognitive measures. Social cognitive and executive function did not significantly differ overall between depressed patients and healthy controls. There was no association between executive function and social cognitive function in healthy controls or in remitted patients. However, in patients with a current state of depression, lower cognitive flexibility was associated with lower performance in facial-affect recognition, theory-of-mind tasks and overall affect recognition. In this group, better planning abilities were associated with decreased performance in facial affect recognition and overall social cognitive performance. While we infer that less cognitive flexibility might lead to a more rigid interpretation of ambiguous social stimuli, we interpret the counterintuitive negative correlation of planning ability and social cognition as a compensatory mechanism. Copyright © 2018. Published by Elsevier B.V.

Interdependence of Inhibitor Recognition in HIV-1 Protease

PubMed Central

2017-01-01

Molecular recognition is a highly interdependent process. Subsite couplings within the active site of proteases are most often revealed through conditional amino acid preferences in substrate recognition. However, the potential effect of these couplings on inhibition and thus inhibitor design is largely unexplored. The present study examines the interdependency of subsites in HIV-1 protease using a focused library of protease inhibitors, to aid in future inhibitor design. Previously a series of darunavir (DRV) analogs was designed to systematically probe the S1′ and S2′ subsites. Co-crystal structures of these analogs with HIV-1 protease provide the ideal opportunity to probe subsite interdependency. All-atom molecular dynamics simulations starting from these structures were performed and systematically analyzed in terms of atomic fluctuations, intermolecular interactions, and water structure. These analyses reveal that the S1′ subsite highly influences other subsites: the extension of the hydrophobic P1′ moiety results in 1) reduced van der Waals contacts in the P2′ subsite, 2) more variability in the hydrogen bond frequencies with catalytic residues and the flap water, and 3) changes in the occupancy of conserved water sites both proximal and distal to the active site. In addition, one of the monomers in this homodimeric enzyme has atomic fluctuations more highly correlated with DRV than the other monomer. These relationships intricately link the HIV-1 protease subsites and are critical to understanding molecular recognition and inhibitor binding. More broadly, the interdependency of subsite recognition within an active site requires consideration in the selection of chemical moieties in drug design; this strategy is in contrast to what is traditionally done with independent optimization of chemical moieties of an inhibitor. PMID:28358514

Interdependence of Inhibitor Recognition in HIV-1 Protease.

PubMed

Paulsen, Janet L; Leidner, Florian; Ragland, Debra A; Kurt Yilmaz, Nese; Schiffer, Celia A

2017-05-09

Molecular recognition is a highly interdependent process. Subsite couplings within the active site of proteases are most often revealed through conditional amino acid preferences in substrate recognition. However, the potential effect of these couplings on inhibition and thus inhibitor design is largely unexplored. The present study examines the interdependency of subsites in HIV-1 protease using a focused library of protease inhibitors, to aid in future inhibitor design. Previously a series of darunavir (DRV) analogs was designed to systematically probe the S1' and S2' subsites. Co-crystal structures of these analogs with HIV-1 protease provide the ideal opportunity to probe subsite interdependency. All-atom molecular dynamics simulations starting from these structures were performed and systematically analyzed in terms of atomic fluctuations, intermolecular interactions, and water structure. These analyses reveal that the S1' subsite highly influences other subsites: the extension of the hydrophobic P1' moiety results in 1) reduced van der Waals contacts in the P2' subsite, 2) more variability in the hydrogen bond frequencies with catalytic residues and the flap water, and 3) changes in the occupancy of conserved water sites both proximal and distal to the active site. In addition, one of the monomers in this homodimeric enzyme has atomic fluctuations more highly correlated with DRV than the other monomer. These relationships intricately link the HIV-1 protease subsites and are critical to understanding molecular recognition and inhibitor binding. More broadly, the interdependency of subsite recognition within an active site requires consideration in the selection of chemical moieties in drug design; this strategy is in contrast to what is traditionally done with independent optimization of chemical moieties of an inhibitor.

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks.

PubMed

Luque, Joaquín; Larios, Diego F; Personal, Enrique; Barbancho, Julio; León, Carlos

2016-05-18

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

PubMed Central

Luque, Joaquín; Larios, Diego F.; Personal, Enrique; Barbancho, Julio; León, Carlos

2016-01-01

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance. PMID:27213375

The influence of writing practice on letter recognition in preschool children: a comparison between handwriting and typing.

PubMed

Longcamp, Marieke; Zerbato-Poudou, Marie-Thérèse; Velay, Jean-Luc

2005-05-01

A large body of data supports the view that movement plays a crucial role in letter representation and suggests that handwriting contributes to the visual recognition of letters. If so, changing the motor conditions while children are learning to write by using a method based on typing instead of handwriting should affect their subsequent letter recognition performances. In order to test this hypothesis, we trained two groups of 38 children (aged 3-5 years) to copy letters of the alphabet either by hand or by typing them. After three weeks of learning, we ran two recognition tests, one week apart, to compare the letter recognition performances of the two groups. The results showed that in the older children, the handwriting training gave rise to a better letter recognition than the typing training.

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network

PubMed Central

Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin. PMID:27258404

Low energy physical activity recognition system on smartphones.

PubMed

Soria Morillo, Luis Miguel; Gonzalez-Abril, Luis; Ortega Ramirez, Juan Antonio; de la Concepcion, Miguel Angel Alvarez

2015-03-03

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.

[Recognition of facial emotions and theory of mind in schizophrenia: could the theory of mind deficit be due to the non-recognition of facial emotions?].

PubMed

Besche-Richard, C; Bourrin-Tisseron, A; Olivier, M; Cuervo-Lombard, C-V; Limosin, F

2012-06-01

The deficits of recognition of facial emotions and attribution of mental states are now well-documented in schizophrenic patients. However, we don't clearly know about the link between these two complex cognitive functions, especially in schizophrenia. In this study, we attempted to test the link between the recognition of facial emotions and the capacities of mentalization, notably the attribution of beliefs, in health and schizophrenic participants. We supposed that the level of performance of recognition of facial emotions, compared to the working memory and executive functioning, was the best predictor of the capacities to attribute a belief. Twenty schizophrenic participants according to DSM-IVTR (mean age: 35.9 years, S.D. 9.07; mean education level: 11.15 years, S.D. 2.58) clinically stabilized, receiving neuroleptic or antipsychotic medication participated in the study. They were matched on age (mean age: 36.3 years, S.D. 10.9) and educational level (mean educational level: 12.10, S.D. 2.25) with 30 matched healthy participants. All the participants were evaluated with a pool of tasks testing the recognition of facial emotions (the faces of Baron-Cohen), the attribution of beliefs (two stories of first order and two stories of second order), the working memory (the digit span of the WAIS-III and the Corsi test) and the executive functioning (Trail Making Test A et B, Wisconsin Card Sorting Test brief version). Comparing schizophrenic and healthy participants, our results confirmed a difference between the performances of the recognition of facial emotions and those of the attribution of beliefs. The result of the simple linear regression showed that the recognition of facial emotions, compared to the performances of working memory and executive functioning, was the best predictor of the performances in the theory of mind stories. Our results confirmed, in a sample of schizophrenic patients, the deficits in the recognition of facial emotions and in the attribution of mental states. Our new result concerned the demonstration that the performances in the recognition of facial emotions are the best predictor of the performances in the attribution of beliefs. With Marshall et al.'s model on empathy, we can explain this link between the recognition of facial emotions and the comprehension of beliefs. Copyright © 2011 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

[How are consumers, service and market factors related to customer loyalty in medical service? Targeting the medical consumer in a city].

PubMed

Lee, Sunhee; Kim, Hyunmi; Kim, Juhye; Ha, Gwiyeom

2008-09-01

This study was performed to explore customer loyalty and the related factors. 900 households (a 1% sample) were randomly selected from the total population of K city located in Kangwon province. An interview survey was performed with using a structured questionnaire for the subjects (923 persons) who had used medical service during the year before the survey, and the survey was done September, 2002. When comparing the relating factors related with customer loyalty according to the sociodemographic characteristics, the older group showed a significantly higher level of recognition for service quality, service reputation, internal customers.attitudes and switching cost. The lower income group showed a higher level of recognition for service quality, service image and switching cost. The lower educated group showed a higher level of recognition for service reputation, service image and internal customers.attitudes. The higher educated group showed a higher level of recognition for perceived risk, and seeking variety. In addition, the expert group or the service and manufacturing workers group showed a higher level of recognition for service involvement. On multiple regression analysis, internal customers' attitudes, service image, service reputation, service quality, switching cost, and substitutability showed significant relations with customer loyalty. This study showed that customer loyalty was significantly influenced by service factors like internal customers' attitudes, service image, service reputation, and service quality, and by market factors like switching cost, and substitutability. The results of this study can be used as a baseline for developing strategies to create and keep customers with high loyalty.

Right Limbic FDG-PET Hypometabolism Correlates with Emotion Recognition and Attribution in Probable Behavioral Variant of Frontotemporal Dementia Patients

PubMed Central

Cerami, Chiara; Dodich, Alessandra; Iannaccone, Sandro; Marcone, Alessandra; Lettieri, Giada; Crespi, Chiara; Gianolli, Luigi; Cappa, Stefano F.; Perani, Daniela

2015-01-01

The behavioural variant of frontotemporal dementia (bvFTD) is a rare disease mainly affecting the social brain. FDG-PET fronto-temporal hypometabolism is a supportive feature for the diagnosis. It may also provide specific functional metabolic signatures for altered socio-emotional processing. In this study, we evaluated the emotion recognition and attribution deficits and FDG-PET cerebral metabolic patterns at the group and individual levels in a sample of sporadic bvFTD patients, exploring the cognitive-functional correlations. Seventeen probable mild bvFTD patients (10 male and 7 female; age 67.8±9.9) were administered standardized and validated version of social cognition tasks assessing the recognition of basic emotions and the attribution of emotions and intentions (i.e., Ekman 60-Faces test-Ek60F and Story-based Empathy task-SET). FDG-PET was analysed using an optimized voxel-based SPM method at the single-subject and group levels. Severe deficits of emotion recognition and processing characterized the bvFTD condition. At the group level, metabolic dysfunction in the right amygdala, temporal pole, and middle cingulate cortex was highly correlated to the emotional recognition and attribution performances. At the single-subject level, however, heterogeneous impairments of social cognition tasks emerged, and different metabolic patterns, involving limbic structures and prefrontal cortices, were also observed. The derangement of a right limbic network is associated with altered socio-emotional processing in bvFTD patients, but different hypometabolic FDG-PET patterns and heterogeneous performances on social tasks at an individual level exist. PMID:26513651

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

Evaluation of a wireless audio streaming accessory to improve mobile telephone performance of cochlear implant users.

PubMed

Wolfe, Jace; Morais Duke, Mila; Schafer, Erin; Cire, George; Menapace, Christine; O'Neill, Lori

2016-01-01

The objective of this study was to evaluate the potential improvement in word recognition in quiet and in noise obtained with use of a Bluetooth-compatible wireless hearing assistance technology (HAT) relative to the acoustic mobile telephone condition (e.g. the mobile telephone receiver held to the microphone of the sound processor). A two-way repeated measures design was used to evaluate differences in telephone word recognition obtained in quiet and in competing noise in the acoustic mobile telephone condition compared to performance obtained with use of the CI sound processor and a telephone HAT. Sixteen adult users of Nucleus cochlear implants and the Nucleus 6 sound processor were included in this study. Word recognition over the mobile telephone in quiet and in noise was significantly better with use of the wireless HAT compared to performance in the acoustic mobile telephone condition. Word recognition over the mobile telephone was better in quiet when compared to performance in noise. The results of this study indicate that use of a wireless HAT improves word recognition over the mobile telephone in quiet and in noise relative to performance in the acoustic mobile telephone condition for a group of adult cochlear implant recipients.

Evaluating the accuracy of the Wechsler Memory Scale-Fourth Edition (WMS-IV) logical memory embedded validity index for detecting invalid test performance.

PubMed

Soble, Jason R; Bain, Kathleen M; Bailey, K Chase; Kirton, Joshua W; Marceaux, Janice C; Critchfield, Edan A; McCoy, Karin J M; O'Rourke, Justin J F

2018-01-08

Embedded performance validity tests (PVTs) allow for continuous assessment of invalid performance throughout neuropsychological test batteries. This study evaluated the utility of the Wechsler Memory Scale-Fourth Edition (WMS-IV) Logical Memory (LM) Recognition score as an embedded PVT using the Advanced Clinical Solutions (ACS) for WAIS-IV/WMS-IV Effort System. This mixed clinical sample was comprised of 97 total participants, 71 of whom were classified as valid and 26 as invalid based on three well-validated, freestanding criterion PVTs. Overall, the LM embedded PVT demonstrated poor concordance with the criterion PVTs and unacceptable psychometric properties using ACS validity base rates (42% sensitivity/79% specificity). Moreover, 15-39% of participants obtained an invalid ACS base rate despite having a normatively-intact age-corrected LM Recognition total score. Receiving operating characteristic curve analysis revealed a Recognition total score cutoff of < 61% correct improved specificity (92%) while sensitivity remained weak (31%). Thus, results indicated the LM Recognition embedded PVT is not appropriate for use from an evidence-based perspective, and that clinicians may be faced with reconciling how a normatively intact cognitive performance on the Recognition subtest could simultaneously reflect invalid performance validity.

Preparation and recognition performance of creatinine-imprinted material prepared with novel surface-imprinting technique.

PubMed

Gao, Baojiao; Li, Yanbin; Zhang, Zhenguo

2010-08-01

By adopting the novel surface molecular imprinting technique put forward by us not long ago, a creatinine molecule-imprinted material with high performance was prepared. The functional macromolecule polymethacrylic acid (PMAA) was first grafted on the surfaces of micron-sized silica gel particles in the manner of "grafting from" using 3-methacryloxypropyltrimethoxysilane (MPS) as intermedia, resulting in the grafted particles PMAA/SiO(2). Subsequently, the molecular imprinting was carried out towards the grafted macromolecule PMAA using creatinine as template and with ethylene glycol diglycidyl ether (EGGE) as crosslinker by right of the intermolecular hydrogen bonding and electrostatic interaction between the grafted PMAA and creatinine molecules. Finally, the creatinine-imprinted material MIP-PMAA/SiO(2) was obtained. The binding character of MIP-PMAA/SiO(2) for creatinine was investigated in depth with both batch and column methods and using N-hydroxysuccinimide and creatine as two contrast substances, whose chemical structures are similar to creatinine to a certain degree. The experimental results show that the surface-imprinted material MIP-PMAA/SiO(2) has excellent binding affinity and high recognition selectivity for creatinine. Before imprinting, PMAA/SiO(2) particles nearly has not recognition selectivity for creatinine, and the selectivity coefficients of PMAA/SiO(2) for creatinine relative to N-hydroxysuccinimide and creatine are only 1.23 and 1.30, respectively. However, after imprinting, the selectivity coefficients of MIP-PMAA/SiO(2) for creatinine in respect to N-hydroxysuccinimide and creatine are remarkably enhanced to 11.64 and 12.87, respectively, displaying the excellent recognition selectivity and binding affinity towards creatinine molecules. Copyright 2010 Elsevier B.V. All rights reserved.

Fusiform gyrus volume reduction and facial recognition in chronic schizophrenia.

PubMed

Onitsuka, Toshiaki; Shenton, Martha E; Kasai, Kiyoto; Nestor, Paul G; Toner, Sarah K; Kikinis, Ron; Jolesz, Ferenc A; McCarley, Robert W

2003-04-01

The fusiform gyrus (FG), or occipitotemporal gyrus, is thought to subserve the processing and encoding of faces. Of note, several studies have reported that patients with schizophrenia show deficits in facial processing. It is thus hypothesized that the FG might be one brain region underlying abnormal facial recognition in schizophrenia. The objectives of this study were to determine whether there are abnormalities in gray matter volumes for the anterior and the posterior FG in patients with chronic schizophrenia and to investigate relationships between FG subregions and immediate and delayed memory for faces. Patients were recruited from the Boston VA Healthcare System, Brockton Division, and control subjects were recruited through newspaper advertisement. Study participants included 21 male patients diagnosed as having chronic schizophrenia and 28 male controls. Participants underwent high-spatial-resolution magnetic resonance imaging, and facial recognition memory was evaluated. Main outcome measures included anterior and posterior FG gray matter volumes based on high-spatial-resolution magnetic resonance imaging, a detailed and reliable manual delineation using 3-dimensional information, and correlation coefficients between FG subregions and raw scores on immediate and delayed facial memory derived from the Wechsler Memory Scale III. Patients with chronic schizophrenia had overall smaller FG gray matter volumes (10%) than normal controls. Additionally, patients with schizophrenia performed more poorly than normal controls in both immediate and delayed facial memory tests. Moreover, the degree of poor performance on delayed memory for faces was significantly correlated with the degree of bilateral anterior FG reduction in patients with schizophrenia. These results suggest that neuroanatomic FG abnormalities underlie at least some of the deficits associated with facial recognition in schizophrenia.

The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

PubMed Central

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Rymer, William Zev; Zhou, Ping

2013-01-01

This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

Bayesian Analysis of Recognition Memory: The Case of the List-Length Effect

ERIC Educational Resources Information Center

Dennis, Simon; Lee, Michael D.; Kinnell, Angela

2008-01-01

Recognition memory experiments are an important source of empirical constraints for theories of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence…

Thermal-to-visible face recognition using partial least squares.

PubMed

Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

2015-03-01

Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios.

Multitasking During Degraded Speech Recognition in School-Age Children

PubMed Central

Ward, Kristina M.; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children’s multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children’s accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children’s dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children’s proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition. PMID:28105890

Family environment influences emotion recognition following paediatric traumatic brain injury

PubMed Central

SCHMIDT, ADAM T.; ORSTEN, KIMBERLEY D.; HANTEN, GERRI R.; LI, XIAOQI; LEVIN, HARVEY S.

2011-01-01

Objective This study investigated the relationship between family functioning and performance on two tasks of emotion recognition (emotional prosody and face emotion recognition) and a cognitive control procedure (the Flanker task) following paediatric traumatic brain injury (TBI) or orthopaedic injury (OI). Methods A total of 142 children (75 TBI, 67 OI) were assessed on three occasions: baseline, 3 months and 1 year post-injury on the two emotion recognition tasks and the Flanker task. Caregivers also completed the Life Stressors and Resources Scale (LISRES) on each occasion. Growth curve analysis was used to analyse the data. Results Results indicated that family functioning influenced performance on the emotional prosody and Flanker tasks but not on the face emotion recognition task. Findings on both the emotional prosody and Flanker tasks were generally similar across groups. However, financial resources emerged as significantly related to emotional prosody performance in the TBI group only (p = 0.0123). Conclusions Findings suggest family functioning variables—especially financial resources—can influence performance on an emotional processing task following TBI in children. PMID:21058900

Multitasking During Degraded Speech Recognition in School-Age Children.

PubMed

Grieco-Calub, Tina M; Ward, Kristina M; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children's multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children's accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children's dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children's proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition.

Activity Recognition in Social Media

DTIC Science & Technology

2015-12-29

AFRL-AFOSR-JP-TR-2016-0044 Activity Recognition in Social Media Subhasis Chaudhuri INDIAN INSTITUTE OF TECHNOLOGY BOMBAY Final Report 05/09/2016...DATES COVERED (From - To) 12 Aug 2013 to 30 Sep 2015 4. TITLE AND SUBTITLE Activity Recognition in Social Media 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) INDIAN INSTITUTE OF TECHNOLOGY BOMBAY POWAI MUMBAI, 400076 IN 8. PERFORMING ORGANIZATION REPORT NUMBER

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

New Optical Transforms For Statistical Image Recognition

NASA Astrophysics Data System (ADS)

Lee, Sing H.

1983-12-01

In optical implementation of statistical image recognition, new optical transforms on large images for real-time recognition are of special interest. Several important linear transformations frequently used in statistical pattern recognition have now been optically implemented, including the Karhunen-Loeve transform (KLT), the Fukunaga-Koontz transform (FKT) and the least-squares linear mapping technique (LSLMT).1-3 The KLT performs principle components analysis on one class of patterns for feature extraction. The FKT performs feature extraction for separating two classes of patterns. The LSLMT separates multiple classes of patterns by maximizing the interclass differences and minimizing the intraclass variations.

Literature review of voice recognition and generation technology for Army helicopter applications

NASA Astrophysics Data System (ADS)

Christ, K. A.

1984-08-01

This report is a literature review on the topics of voice recognition and generation. Areas covered are: manual versus vocal data input, vocabulary, stress and workload, noise, protective masks, feedback, and voice warning systems. Results of the studies presented in this report indicate that voice data entry has less of an impact on a pilot's flight performance, during low-level flying and other difficult missions, than manual data entry. However, the stress resulting from such missions may cause the pilot's voice to change, reducing the recognition accuracy of the system. The noise present in helicopter cockpits also causes the recognition accuracy to decrease. Noise-cancelling devices are being developed and improved upon to increase the recognition performance in noisy environments. Future research in the fields of voice recognition and generation should be conducted in the areas of stress and workload, vocabulary, and the types of voice generation best suited for the helicopter cockpit. Also, specific tasks should be studied to determine whether voice recognition and generation can be effectively applied.

Understanding gender bias in face recognition: effects of divided attention at encoding.

PubMed

Palmer, Matthew A; Brewer, Neil; Horry, Ruth

2013-03-01

Prior research has demonstrated a female own-gender bias in face recognition, with females better at recognizing female faces than male faces. We explored the basis for this effect by examining the effect of divided attention during encoding on females' and males' recognition of female and male faces. For female participants, divided attention impaired recognition performance for female faces to a greater extent than male faces in a face recognition paradigm (Study 1; N=113) and an eyewitness identification paradigm (Study 2; N=502). Analysis of remember-know judgments (Study 2) indicated that divided attention at encoding selectively reduced female participants' recollection of female faces at test. For male participants, divided attention selectively reduced recognition performance (and recollection) for male stimuli in Study 2, but had similar effects on recognition of male and female faces in Study 1. Overall, the results suggest that attention at encoding contributes to the female own-gender bias by facilitating the later recollection of female faces. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of cholinergic deafferentation of the rhinal cortex on visual recognition memory in monkeys.

PubMed

Turchi, Janita; Saunders, Richard C; Mishkin, Mortimer

2005-02-08

Excitotoxic lesion studies have confirmed that the rhinal cortex is essential for visual recognition ability in monkeys. To evaluate the mnemonic role of cholinergic inputs to this cortical region, we compared the visual recognition performance of monkeys given rhinal cortex infusions of a selective cholinergic immunotoxin, ME20.4-SAP, with the performance of monkeys given control infusions into this same tissue. The immunotoxin, which leads to selective cholinergic deafferentation of the infused cortex, yielded recognition deficits of the same magnitude as those produced by excitotoxic lesions of this region, providing the most direct demonstration to date that cholinergic activation of the rhinal cortex is essential for storing the representations of new visual stimuli and thereby enabling their later recognition.

Blood perfusion construction for infrared face recognition based on bio-heat transfer.

PubMed

Xie, Zhihua; Liu, Guodong

2014-01-01

To improve the performance of infrared face recognition for time-lapse data, a new construction of blood perfusion is proposed based on bio-heat transfer. Firstly, by quantifying the blood perfusion based on Pennes equation, the thermal information is converted into blood perfusion rate, which is stable facial biological feature of face image. Then, the separability discriminant criterion in Discrete Cosine Transform (DCT) domain is applied to extract the discriminative features of blood perfusion information. Experimental results demonstrate that the features of blood perfusion are more concentrative and discriminative for recognition than those of thermal information. The infrared face recognition based on the proposed blood perfusion is robust and can achieve better recognition performance compared with other state-of-the-art approaches.

Listening Effort and Speech Recognition with Frequency Compression Amplification for Children and Adults with Hearing Loss.

PubMed

Brennan, Marc A; Lewis, Dawna; McCreery, Ryan; Kopun, Judy; Alexander, Joshua M

2017-10-01

Nonlinear frequency compression (NFC) can improve the audibility of high-frequency sounds by lowering them to a frequency where audibility is better; however, this lowering results in spectral distortion. Consequently, performance is a combination of the effects of increased access to high-frequency sounds and the detrimental effects of spectral distortion. Previous work has demonstrated positive benefits of NFC on speech recognition when NFC is set to improve audibility while minimizing distortion. However, the extent to which NFC impacts listening effort is not well understood, especially for children with sensorineural hearing loss (SNHL). To examine the impact of NFC on recognition and listening effort for speech in adults and children with SNHL. Within-subject, quasi-experimental study. Participants listened to amplified nonsense words that were (1) frequency-lowered using NFC, (2) low-pass filtered at 5 kHz to simulate the restricted bandwidth (RBW) of conventional hearing aid processing, or (3) low-pass filtered at 10 kHz to simulate extended bandwidth (EBW) amplification. Fourteen children (8-16 yr) and 14 adults (19-65 yr) with mild-to-severe SNHL. Participants listened to speech processed by a hearing aid simulator that amplified input signals to fit a prescriptive target fitting procedure. Participants were blinded to the type of processing. Participants' responses to each nonsense word were analyzed for accuracy and verbal-response time (VRT; listening effort). A multivariate analysis of variance and linear mixed model were used to determine the effect of hearing-aid signal processing on nonsense word recognition and VRT. Both children and adults identified the nonsense words and initial consonants better with EBW and NFC than with RBW. The type of processing did not affect the identification of the vowels or final consonants. There was no effect of age on recognition of the nonsense words, initial consonants, medial vowels, or final consonants. VRT did not change significantly with the type of processing or age. Both adults and children demonstrated improved speech recognition with access to the high-frequency sounds in speech. Listening effort as measured by VRT was not affected by access to high-frequency sounds. American Academy of Audiology

Applying Suffix Rules to Organization Name Recognition

NASA Astrophysics Data System (ADS)

Inui, Takashi; Murakami, Koji; Hashimoto, Taiichi; Utsumi, Kazuo; Ishikawa, Masamichi

This paper presents a method for boosting the performance of the organization name recognition, which is a part of named entity recognition (NER). Although gazetteers (lists of the NEs) have been known as one of the effective features for supervised machine learning approaches on the NER task, the previous methods which have applied the gazetteers to the NER were very simple. The gazetteers have been used just for searching the exact matches between input text and NEs included in them. The proposed method generates regular expression rules from gazetteers, and, with these rules, it can realize a high-coverage searches based on looser matches between input text and NEs. To generate these rules, we focus on the two well-known characteristics of NE expressions; 1) most of NE expressions can be divided into two parts, class-reference part and instance-reference part, 2) for most of NE expressions the class-reference parts are located at the suffix position of them. A pattern mining algorithm runs on the set of NEs in the gazetteers, and some frequent word sequences from which NEs are constructed are found. Then, we employ only word sequences which have the class-reference part at the suffix position as suffix rules. Experimental results showed that our proposed method improved the performance of the organization name recognition, and achieved the 84.58 F-value for evaluation data.

Method of determining the necessary number of observations for video stream documents recognition

NASA Astrophysics Data System (ADS)

Arlazarov, Vladimir V.; Bulatov, Konstantin; Manzhikov, Temudzhin; Slavin, Oleg; Janiszewski, Igor

2018-04-01

This paper discusses a task of document recognition on a sequence of video frames. In order to optimize the processing speed an estimation is performed of stability of recognition results obtained from several video frames. Considering identity document (Russian internal passport) recognition on a mobile device it is shown that significant decrease is possible of the number of observations necessary for obtaining precise recognition result.

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

Recognition of names of eminent psychologists.

PubMed

Duncan, C P

1976-10-01

Faculty members, graduate students, undergraduate majors, and introductory psychology students checked those names they recognized in the list of 228 deceased psychologists, rated for eminence, provided by Annin, Boring, and Watson. Mean percentage recognition was less than 50% for the 128 American psychologists, and less than 25% for the 100 foreign psychologists, by the faculty subjects. The other three groups of subjects gave even lower recognition scores. Recognition was probably also influenced by recency; median year of death of the American psychologists was 1955, of the foreign psychologists, 1943. High recognition (defined as recognition by 80% or more of the faculty group) was achieved by only 34 psychologists, almost all of them American. These highly recognized psychologists also had high eminence ratings, but there was an equal number of psychologists with high eminence ratings that were poorly recognized.

High-speed railway real-time localization auxiliary method based on deep neural network

NASA Astrophysics Data System (ADS)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

Do Adults with High Functioning Autism or Asperger Syndrome Differ in Empathy and Emotion Recognition?

PubMed

Montgomery, Charlotte B; Allison, Carrie; Lai, Meng-Chuan; Cassidy, Sarah; Langdon, Peter E; Baron-Cohen, Simon

2016-06-01

The present study examined whether adults with high functioning autism (HFA) showed greater difficulties in (1) their self-reported ability to empathise with others and/or (2) their ability to read mental states in others' eyes than adults with Asperger syndrome (AS). The Empathy Quotient (EQ) and 'Reading the Mind in the Eyes' Test (Eyes Test) were compared in 43 adults with AS and 43 adults with HFA. No significant difference was observed on EQ score between groups, while adults with AS performed significantly better on the Eyes Test than those with HFA. This suggests that adults with HFA may need more support, particularly in mentalizing and complex emotion recognition, and raises questions about the existence of subgroups within autism spectrum conditions.

Social competence intervention for youth with Asperger Syndrome and high-functioning autism: an initial investigation.

PubMed

Stichter, Janine P; Herzog, Melissa J; Visovsky, Karen; Schmidt, Carla; Randolph, Jena; Schultz, Tia; Gage, Nicholas

2010-09-01

Individuals with high functioning autism (HFA) or Asperger Syndrome (AS) exhibit difficulties in the knowledge or correct performance of social skills. This subgroup's social difficulties appear to be associated with deficits in three social cognition processes: theory of mind, emotion recognition and executive functioning. The current study outlines the development and initial administration of the group-based Social Competence Intervention (SCI), which targeted these deficits using cognitive behavioral principles. Across 27 students age 11-14 with a HFA/AS diagnosis, results indicated significant improvement on parent reports of social skills and executive functioning. Participants evidenced significant growth on direct assessments measuring facial expression recognition, theory of mind and problem solving. SCI appears promising, however, larger samples and application in naturalistic settings are warranted.

Validating a two-high-threshold measurement model for confidence rating data in recognition.

PubMed

Bröder, Arndt; Kellen, David; Schütz, Julia; Rohrmeier, Constanze

2013-01-01

Signal Detection models as well as the Two-High-Threshold model (2HTM) have been used successfully as measurement models in recognition tasks to disentangle memory performance and response biases. A popular method in recognition memory is to elicit confidence judgements about the presumed old/new status of an item, allowing for the easy construction of ROCs. Since the 2HTM assumes fewer latent memory states than response options are available in confidence ratings, the 2HTM has to be extended by a mapping function which models individual rating scale usage. Unpublished data from 2 experiments in Bröder and Schütz (2009) validate the core memory parameters of the model, and 3 new experiments show that the response mapping parameters are selectively affected by manipulations intended to affect rating scale use, and this is independent of overall old/new bias. Comparisons with SDT show that both models behave similarly, a case that highlights the notion that both modelling approaches can be valuable (and complementary) elements in a researcher's toolbox.

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Action Bank: A High Level Representation of Activity in Video (Author’s Manuscript)

DTIC Science & Technology

2012-07-26

of highly discriminative performance. We have tested action bank on four major activity recognition benchmarks. In all cases, our perfor- mance is...that seek a more semantically rich and discriminative Bank of Action Detectors View 1 View 2 View n Biking Javelin Jump Rope Fencing Input Video...Positive: jumping, throwing , running, ... Negative: biking, fencing, drumming, ... Figure 1. Action bank is a high-level representation for video ac

Familial covariation of facial emotion recognition and IQ in schizophrenia.

PubMed

Andric, Sanja; Maric, Nadja P; Mihaljevic, Marina; Mirjanic, Tijana; van Os, Jim

2016-12-30

Alterations in general intellectual ability and social cognition in schizophrenia are core features of the disorder, evident at the illness' onset and persistent throughout its course. However, previous studies examining cognitive alterations in siblings discordant for schizophrenia yielded inconsistent results. Present study aimed to investigate the nature of the association between facial emotion recognition and general IQ by applying genetically sensitive cross-trait cross-sibling design. Participants (total n=158; patients, unaffected siblings, controls) were assessed using the Benton Facial Recognition Test, the Degraded Facial Affect Recognition Task (DFAR) and the Wechsler Adult Intelligence Scale-III. Patients had lower IQ and altered facial emotion recognition in comparison to other groups. Healthy siblings and controls did not significantly differ in IQ and DFAR performance, but siblings exhibited intermediate angry facial expression recognition. Cross-trait within-subject analyses showed significant associations between overall DFAR performance and IQ in all participants. Within-trait cross-sibling analyses found significant associations between patients' and siblings' IQ and overall DFAR performance, suggesting their familial clustering. Finally, cross-trait cross-sibling analyses revealed familial covariation of facial emotion recognition and IQ in siblings discordant for schizophrenia, further indicating their familial etiology. Both traits are important phenotypes for genetic studies and potential early clinical markers of schizophrenia-spectrum disorders. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Global precedence effects account for individual differences in both face and object recognition performance.

PubMed

Gerlach, Christian; Starrfelt, Randi

2018-03-20

There has been an increase in studies adopting an individual difference approach to examine visual cognition and in particular in studies trying to relate face recognition performance with measures of holistic processing (the face composite effect and the part-whole effect). In the present study we examine whether global precedence effects, measured by means of non-face stimuli in Navon's paradigm, can also account for individual differences in face recognition and, if so, whether the effect is of similar magnitude for faces and objects. We find evidence that global precedence effects facilitate both face and object recognition, and to a similar extent. Our results suggest that both face and object recognition are characterized by a coarse-to-fine temporal dynamic, where global shape information is derived prior to local shape information, and that the efficiency of face and object recognition is related to the magnitude of the global precedence effect.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

The relationship between face recognition ability and socioemotional functioning throughout adulthood.

PubMed

Turano, Maria Teresa; Viggiano, Maria Pia

2017-11-01

The relationship between face recognition ability and socioemotional functioning has been widely explored. However, how aging modulates this association regarding both objective performance and subjective-perception is still neglected. Participants, aged between 18 and 81 years, performed a face memory test and completed subjective face recognition and socioemotional questionnaires. General and social anxiety, and neuroticism traits account for the individual variation in face recognition abilities during adulthood. Aging modulates these relationships because as they age, individuals that present a higher level of these traits also show low-level face recognition ability. Intriguingly, the association between depression and face recognition abilities is evident with increasing age. Overall, the present results emphasize the importance of embedding face metacognition measurement into the context of these studies and suggest that aging is an important factor to be considered, which seems to contribute to the relationship between socioemotional and face-cognitive functioning.

AstroCV: Astronomy computer vision library

NASA Astrophysics Data System (ADS)

González, Roberto E.; Muñoz, Roberto P.; Hernández, Cristian A.

2018-04-01

AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

DTIC Science & Technology

2008-01-01

Parallel architectures and algorithms for image understanding. Boston: Academic Press, 1991. [99] A. Bruhn, T. Jakob, M. Fischer, T. Kohlberger , J...Symposium on Pattern Recognition, vol. 2449(pp. 290-297, 2002. [100] A. Bruhn, T. Jakob, M. Fischer, T. Kohlberger , J. Weickert, U. Bruning, and C

Psychological safety: The key to high performance in high stress, potentially traumatic environments

Treesearch

James Saveland

2011-01-01

Safety is typically talked about in a context of the absence of injury. The field of resilience engineering has been advocating that we think about safety differently, by taking a systems view and begin to see how people create safety in unsafe systems by managing risk. There is growing recognition that safety is an emergent behavior of our complex system of human...

Latency of modality-specific reactivation of auditory and visual information during episodic memory retrieval.

PubMed

Ueno, Daisuke; Masumoto, Kouhei; Sutani, Kouichi; Iwaki, Sunao

2015-04-15

This study used magnetoencephalography (MEG) to examine the latency of modality-specific reactivation in the visual and auditory cortices during a recognition task to determine the effects of reactivation on episodic memory retrieval. Nine right-handed healthy young adults participated in the experiment. The experiment consisted of a word-encoding phase and two recognition phases. Three encoding conditions were included: encoding words alone (word-only) and encoding words presented with either related pictures (visual) or related sounds (auditory). The recognition task was conducted in the MEG scanner 15 min after the completion of the encoding phase. After the recognition test, a source-recognition task was given, in which participants were required to choose whether each recognition word was not presented or was presented with which information during the encoding phase. Word recognition in the auditory condition was higher than that in the word-only condition. Confidence-of-recognition scores (d') and the source-recognition test showed superior performance in both the visual and the auditory conditions compared with the word-only condition. An equivalent current dipoles analysis of MEG data indicated that higher equivalent current dipole amplitudes in the right fusiform gyrus occurred during the visual condition and in the superior temporal auditory cortices during the auditory condition, both 450-550 ms after onset of the recognition stimuli. Results suggest that reactivation of visual and auditory brain regions during recognition binds language with modality-specific information and that reactivation enhances confidence in one's recognition performance.

Speed, Dissipation, and Accuracy in Early T-cell Recognition

NASA Astrophysics Data System (ADS)

Cui, Wenping; Mehta, Pankaj

In the immune system, T cells can perform self-foreign discrimination with great foreign ligand sensitivity, high decision speed and low energy cost. There is significant evidence T-cells achieve such great performance with a mechanism: kinetic proofreading(KPR). KPR-based mechanisms actively consume energy to increase the specificity of T-cell recognition. An important theoretical question arises: how to understand trade-offs and fundamental limits on accuracy, speed, and dissipation (energy consumption). Recent theoretical work suggests that it is always possible to reduce the the error of KPR-based mechanisms by waiting longer and/or consuming more energy. Surprisingly, we find that this is not the case and that there actually exists an optimal point in the speed-energy-accuracy plane for KPR and its generalizations. This work was supported by NIH R35 and Simons MMLS Grant.

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery.

PubMed

Casado, Monica Rivas; Gonzalez, Rocio Ballesteros; Kriechbaumer, Thomas; Veal, Amanda

2015-11-04

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

Melodic contour identification by cochlear implant listeners.

PubMed

Galvin, John J; Fu, Qian-Jie; Nogaki, Geraldine

2007-06-01

While the cochlear implant provides many deaf patients with good speech understanding in quiet, music perception and appreciation with the cochlear implant remains a major challenge for most cochlear implant users. The present study investigated whether a closed-set melodic contour identification (MCI) task could be used to quantify cochlear implant users' ability to recognize musical melodies and whether MCI performance could be improved with moderate auditory training. The present study also compared MCI performance with familiar melody identification (FMI) performance, with and without MCI training. For the MCI task, test stimuli were melodic contours composed of 5 notes of equal duration whose frequencies corresponded to musical intervals. The interval between successive notes in each contour was varied between 1 and 5 semitones; the "root note" of the contours was also varied (A3, A4, and A5). Nine distinct musical patterns were generated for each interval and root note condition, resulting in a total of 135 musical contours. The identification of these melodic contours was measured in 11 cochlear implant users. FMI was also evaluated in the same subjects; recognition of 12 familiar melodies was tested with and without rhythm cues. MCI was also trained in 6 subjects, using custom software and melodic contours presented in a different frequency range from that used for testing. Results showed that MCI recognition performance was highly variable among cochlear implant users, ranging from 14% to 91% correct. For most subjects, MCI performance improved as the number of semitones between successive notes was increased; performance was slightly lower for the A3 root note condition. Mean FMI performance was 58% correct when rhythm cues were preserved and 29% correct when rhythm cues were removed. Statistical analyses revealed no significant correlation between MCI performance and FMI performance (with or without rhythmic cues). However, MCI performance was significantly correlated with vowel recognition performance; FMI performance was not correlated with cochlear implant subjects' phoneme recognition performance. Preliminary results also showed that the MCI training improved all subjects' MCI performance; the improved MCI performance also generalized to improved FMI performance. Preliminary data indicate that the closed-set MCI task is a viable approach toward quantifying an important component of cochlear implant users' music perception. The improvement in MCI performance and generalization to FMI performance with training suggests that MCI training may be useful for improving cochlear implant users' music perception and appreciation; such training may be necessary to properly evaluate patient performance, as acute measures may underestimate the amount of musical information transmitted by the cochlear implant device and received by cochlear implant listeners.

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

NASA Astrophysics Data System (ADS)

Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

2010-02-01

In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

Postlingual adult performance in noise with HiRes 120 and ClearVoice Low, Medium, and High.

PubMed

Holden, Laura K; Brenner, Christine; Reeder, Ruth M; Firszt, Jill B

2013-11-01

The study's objectives were to evaluate speech recognition in multiple listening conditions using several noise types with HiRes 120 and ClearVoice (Low, Medium, High) and to determine which ClearVoice program was most beneficial for everyday use. Fifteen postlingual adults attended four sessions; speech recognition was assessed at sessions 1 and 3 with HiRes 120 and at sessions 2 and 4 with all ClearVoice programs. Test measures included sentences presented in restaurant noise (R-SPACE), in speech-spectrum noise, in four- and eight-talker babble, and connected discourse presented in 12-talker babble. Participants completed a questionnaire comparing ClearVoice programs. Significant group differences in performance between HiRes 120 and ClearVoice were present only in the R-SPACE; performance was better with ClearVoice High than HiRes 120. Among ClearVoice programs, no significant group differences were present for any measure. Individual results revealed most participants performed better in the R-SPACE with ClearVoice than HiRes 120. For other measures, significant individual differences between HiRes 120 and ClearVoice were not prevalent. Individual results among ClearVoice programs differed and overall preferences varied. Questionnaire data indicated increased understanding with High and Medium in certain environments. R-SPACE and questionnaire results indicated an advantage for ClearVoice High and Medium. Individual test and preference data showed mixed results between ClearVoice programs making global recommendations difficult; however, results suggest providing ClearVoice High and Medium and HiRes 120 as processor options for adults willing to change settings. For adults unwilling or unable to change settings, ClearVoice Medium is a practical choice for daily listening.

Individual Differences in Holistic Processing Predict the Own-Race Advantage in Recognition Memory

PubMed Central

DeGutis, Joseph; Mercado, Rogelio J.; Wilmer, Jeremy; Rosenblatt, Andrew

2013-01-01

Individuals are consistently better at recognizing own-race faces compared to other-race faces (other-race effect, ORE). One popular hypothesis is that this recognition memory ORE is caused by differential own- and other-race holistic processing, the simultaneous integration of part and configural face information into a coherent whole. Holistic processing may create a more rich, detailed memory representation of own-race faces compared to other-race faces. Despite several studies showing that own-race faces are processed more holistically than other-race faces, studies have yet to link the holistic processing ORE and the recognition memory ORE. In the current study, we sought to use a more valid method of analyzing individual differences in holistic processing by using regression to statistically remove the influence of the control condition (part trials in the part-whole task) from the condition of interest (whole trials in the part-whole task). We also employed regression to separately examine the two components of the ORE: own-race advantage (regressing other-race from own-race performance) and other-race decrement (regressing own-race from other-race performance). First, we demonstrated that own-race faces were processed more holistically than other-race faces, particularly the eye region. Notably, using regression, we showed a significant association between the own-race advantage in recognition memory and the own-race advantage in holistic processing and that these associations were weaker when examining the other-race decrement. We also demonstrated that performance on own- and other-race faces across all of our tasks was highly correlated, suggesting that the differences we found between own- and other-race faces are quantitative rather than qualitative. Together, this suggests that own- and other-race faces recruit largely similar mechanisms, that own-race faces more thoroughly engage holistic processing, and that this greater engagement of holistic processing is significantly associated with the own-race advantage in recognition memory. PMID:23593119

Individual differences in holistic processing predict the own-race advantage in recognition memory.

PubMed

Degutis, Joseph; Mercado, Rogelio J; Wilmer, Jeremy; Rosenblatt, Andrew

2013-01-01

Individuals are consistently better at recognizing own-race faces compared to other-race faces (other-race effect, ORE). One popular hypothesis is that this recognition memory ORE is caused by differential own- and other-race holistic processing, the simultaneous integration of part and configural face information into a coherent whole. Holistic processing may create a more rich, detailed memory representation of own-race faces compared to other-race faces. Despite several studies showing that own-race faces are processed more holistically than other-race faces, studies have yet to link the holistic processing ORE and the recognition memory ORE. In the current study, we sought to use a more valid method of analyzing individual differences in holistic processing by using regression to statistically remove the influence of the control condition (part trials in the part-whole task) from the condition of interest (whole trials in the part-whole task). We also employed regression to separately examine the two components of the ORE: own-race advantage (regressing other-race from own-race performance) and other-race decrement (regressing own-race from other-race performance). First, we demonstrated that own-race faces were processed more holistically than other-race faces, particularly the eye region. Notably, using regression, we showed a significant association between the own-race advantage in recognition memory and the own-race advantage in holistic processing and that these associations were weaker when examining the other-race decrement. We also demonstrated that performance on own- and other-race faces across all of our tasks was highly correlated, suggesting that the differences we found between own- and other-race faces are quantitative rather than qualitative. Together, this suggests that own- and other-race faces recruit largely similar mechanisms, that own-race faces more thoroughly engage holistic processing, and that this greater engagement of holistic processing is significantly associated with the own-race advantage in recognition memory.

Integration of multispectral face recognition and multi-PTZ camera automated surveillance for security applications

NASA Astrophysics Data System (ADS)

Chen, Chung-Hao; Yao, Yi; Chang, Hong; Koschan, Andreas; Abidi, Mongi

2013-06-01

Due to increasing security concerns, a complete security system should consist of two major components, a computer-based face-recognition system and a real-time automated video surveillance system. A computerbased face-recognition system can be used in gate access control for identity authentication. In recent studies, multispectral imaging and fusion of multispectral narrow-band images in the visible spectrum have been employed and proven to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. Thus, we present an automated method that specifies the optimal spectral ranges under the given illumination. Experimental results verify the consistent performance of our algorithm via the observation that an identical set of spectral band images is selected under all tested conditions. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images. In addition, once a person is authorized to enter a restricted area, we still need to continuously monitor his/her activities for the sake of security. Because pantilt-zoom (PTZ) cameras are capable of covering a panoramic area and maintaining high resolution imagery for real-time behavior understanding, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy as compared to Chen and Wang's method [18].

DNA recognition by peptide nucleic acid-modified PCFs: from models to real samples

NASA Astrophysics Data System (ADS)

Selleri, S.; Coscelli, E.; Poli, F.; Passaro, D.; Cucinotta, A.; Lantano, C.; Corradini, R.; Marchelli, R.

2010-04-01

The increased concern, emerged in the last few years, on food products safety has stimulated the research on new techniques for traceability of raw food materials. DNA analysis is one of the most powerful tools for the certification of food quality, and it is presently performed through the polymerase chain reaction technique. Photonic crystal fibers, due to the presence of an array of air holes running along their length, can be exploited for performing DNA recognition by derivatizing hole surfaces and checking hybridization of complementary nucledotide chains in the sample. In this paper the application of a suspended core photonic crystal fiber in the recognition of DNA sequences is discussed. The fiber is characterized in terms of electromagnetic properties by means of a full-vector modal solver based on the finite element method. Then, the performances of the fiber in the recognition of mall synthetic oligonucleotides are discussed, together with a test of the possibility to extend this recognition to samples of DNA of applicative interest, such as olive leaves.

Finger vein verification system based on sparse representation.

PubMed

Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong

2012-09-01

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Word recognition using a lexicon constrained by first/last character decisions

NASA Astrophysics Data System (ADS)

Zhao, Sheila X.; Srihari, Sargur N.

1995-03-01

In lexicon based recognition of machine-printed word images, the size of the lexicon can be quite extensive. The recognition performance is closely related to the size of the lexicon. Recognition performance drops quickly when lexicon size increases. Here, we present an algorithm to improve the word recognition performance by reducing the size of the given lexicon. The algorithm utilizes the information provided by the first and last characters of a word to reduce the size of the given lexicon. Given a word image and a lexicon that contains the word in the image, the first and last characters are segmented and then recognized by a character classifier. The possible candidates based on the results given by the classifier are selected, which give us the sub-lexicon. Then a word shape analysis algorithm is applied to produce the final ranking of the given lexicon. The algorithm was tested on a set of machine- printed gray-scale word images which includes a wide range of print types and qualities.

The Oxytocin Receptor Gene ( OXTR) and Face Recognition.

PubMed

Verhallen, Roeland J; Bosten, Jenny M; Goodbourn, Patrick T; Lawrance-Owen, Adam J; Bargary, Gary; Mollon, J D

2017-01-01

A recent study has linked individual differences in face recognition to rs237887, a single-nucleotide polymorphism (SNP) of the oxytocin receptor gene ( OXTR; Skuse et al., 2014). In that study, participants were assessed using the Warrington Recognition Memory Test for Faces, but performance on Warrington's test has been shown not to rely purely on face recognition processes. We administered the widely used Cambridge Face Memory Test-a purer test of face recognition-to 370 participants. Performance was not significantly associated with rs237887, with 16 other SNPs of OXTR that we genotyped, or with a further 75 imputed SNPs. We also administered three other tests of face processing (the Mooney Face Test, the Glasgow Face Matching Test, and the Composite Face Test), but performance was never significantly associated with rs237887 or with any of the other genotyped or imputed SNPs, after corrections for multiple testing. In addition, we found no associations between OXTR and Autism-Spectrum Quotient scores.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

PubMed

Zhu, Yanan; Ouyang, Qi; Mao, Youdong

2017-07-21

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

An optimized content-aware image retargeting method: toward expanding the perceived visual field of the high-density retinal prosthesis recipients

NASA Astrophysics Data System (ADS)

Li, Heng; Zeng, Yajie; Lu, Zhuofan; Cao, Xiaofei; Su, Xiaofan; Sui, Xiaohong; Wang, Jing; Chai, Xinyu

2018-04-01

Objective. Retinal prosthesis devices have shown great value in restoring some sight for individuals with profoundly impaired vision, but the visual acuity and visual field provided by prostheses greatly limit recipients’ visual experience. In this paper, we employ computer vision approaches to seek to expand the perceptible visual field in patients implanted potentially with a high-density retinal prosthesis while maintaining visual acuity as much as possible. Approach. We propose an optimized content-aware image retargeting method, by introducing salient object detection based on color and intensity-difference contrast, aiming to remap important information of a scene into a small visual field and preserve their original scale as much as possible. It may improve prosthetic recipients’ perceived visual field and aid in performing some visual tasks (e.g. object detection and object recognition). To verify our method, psychophysical experiments, detecting object number and recognizing objects, are conducted under simulated prosthetic vision. As control, we use three other image retargeting techniques, including Cropping, Scaling, and seam-assisted shrinkability. Main results. Results show that our method outperforms in preserving more key features and has significantly higher recognition accuracy in comparison with other three image retargeting methods under the condition of small visual field and low-resolution. Significance. The proposed method is beneficial to expand the perceived visual field of prosthesis recipients and improve their object detection and recognition performance. It suggests that our method may provide an effective option for image processing module in future high-density retinal implants.

A New Adaptive Structural Signature for Symbol Recognition by Using a Galois Lattice as a Classifier.

PubMed

Coustaty, M; Bertet, K; Visani, M; Ogier, J

2011-08-01

In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois lattice as a classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures-that can be seen as dynamic paths which carry high-level information-are robust toward various transformations. They are classified by using a Galois lattice as a classifier. The performance of the proposed approach is evaluated based on the GREC'03 symbol database, and the experimental results we obtain are encouraging.

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

PubMed

Serrano-Gotarredona, Rafael; Oster, Matthias; Lichtsteiner, Patrick; Linares-Barranco, Alejandro; Paz-Vicente, Rafael; Gomez-Rodriguez, Francisco; Camunas-Mesa, Luis; Berner, Raphael; Rivas-Perez, Manuel; Delbruck, Tobi; Liu, Shih-Chii; Douglas, Rodney; Hafliger, Philipp; Jimenez-Moreno, Gabriel; Civit Ballcels, Anton; Serrano-Gotarredona, Teresa; Acosta-Jimenez, Antonio J; Linares-Barranco, Bernabé

2009-09-01

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

Complex auditory behaviour emerges from simple reactive steering

NASA Astrophysics Data System (ADS)

Hedwig, Berthold; Poulet, James F. A.

2004-08-01

The recognition and localization of sound signals is fundamental to acoustic communication. Complex neural mechanisms are thought to underlie the processing of species-specific sound patterns even in animals with simple auditory pathways. In female crickets, which orient towards the male's calling song, current models propose pattern recognition mechanisms based on the temporal structure of the song. Furthermore, it is thought that localization is achieved by comparing the output of the left and right recognition networks, which then directs the female to the pattern that most closely resembles the species-specific song. Here we show, using a highly sensitive method for measuring the movements of female crickets, that when walking and flying each sound pulse of the communication signal releases a rapid steering response. Thus auditory orientation emerges from reactive motor responses to individual sound pulses. Although the reactive motor responses are not based on the song structure, a pattern recognition process may modulate the gain of the responses on a longer timescale. These findings are relevant to concepts of insect auditory behaviour and to the development of biologically inspired robots performing cricket-like auditory orientation.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Multi-subject subspace alignment for non-stationary EEG-based emotion recognition.

PubMed

Chai, Xin; Wang, Qisong; Zhao, Yongping; Liu, Xin; Liu, Dan; Bai, Ou

2018-01-01

Emotion recognition based on EEG signals is a critical component in Human-Machine collaborative environments and psychiatric health diagnoses. However, EEG patterns have been found to vary across subjects due to user fatigue, different electrode placements, and varying impedances, etc. This problem renders the performance of EEG-based emotion recognition highly specific to subjects, requiring time-consuming individual calibration sessions to adapt an emotion recognition system to new subjects. Recently, domain adaptation (DA) strategies have achieved a great deal success in dealing with inter-subject adaptation. However, most of them can only adapt one subject to another subject, which limits their applicability in real-world scenarios. To alleviate this issue, a novel unsupervised DA strategy called Multi-Subject Subspace Alignment (MSSA) is proposed in this paper, which takes advantage of subspace alignment solution and multi-subject information in a unified framework to build personalized models without user-specific labeled data. Experiments on a public EEG dataset known as SEED verify the effectiveness and superiority of MSSA over other state of the art methods for dealing with multi-subject scenarios.