Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

NASA Astrophysics Data System (ADS)

Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

2014-12-01

Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

A familiarity disadvantage for remembering specific images of faces.

PubMed

Armann, Regine G M; Jenkins, Rob; Burton, A Mike

2016-04-01

Familiar faces are remembered better than unfamiliar faces. Furthermore, it is much easier to match images of familiar than unfamiliar faces. These findings could be accounted for by quantitative differences in the ease with which faces are encoded. However, it has been argued that there are also some qualitative differences in familiar and unfamiliar face processing. Unfamiliar faces are held to rely on superficial, pictorial representations, whereas familiar faces invoke more abstract representations. Here we present 2 studies that show, for 1 task, an advantage for unfamiliar faces. In recognition memory, viewers are better able to reject a new picture, if it depicts an unfamiliar face. This rare advantage for unfamiliar faces supports the notion that familiarity brings about some representational changes, and further emphasizes the idea that theoretical accounts of face processing should incorporate familiarity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms.

PubMed

Geradts, Z J; Bijhold, J; Hermsen, R; Murtagh, F

2001-06-01

On the market several systems exist for collecting spent ammunition data for forensic investigation. These databases store images of cartridge cases and the marks on them. Image matching is used to create hit lists that show which marks on a cartridge case are most similar to another cartridge case. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for optimizing the results of image matching. The images are acquired by side light images for the breech face marks and by ring light for the firing pin impression. For these images a standard way of digitizing the images used. For the side light images and ring light images this means that the user has to position the cartridge case in the same position according to a protocol. The positioning is important for the sidelight, since the image that is obtained of a striation mark depends heavily on the angle of incidence of the light. In practice, it appears that the user positions the cartridge case with +/-10 degrees accuracy. We tested our algorithms using 49 cartridge cases of 19 different firearms, where the examiner determined that they were shot with the same firearm. For testing, these images were mixed with a database consisting of approximately 4900 images that were available from the Drugfire database of different calibers.In cases where the registration and the light conditions among those matching pairs was good, a simple computation of the standard deviation of the subtracted gray levels, delivered the best-matched images. For images that were rotated and shifted, we have implemented a "brute force" way of registration. The images are translated and rotated until the minimum of the standard deviation of the difference is found. This method did not result in all relevant matches in the top position. This is caused by the effect that shadows and highlights are compared in intensity. Since the angle of incidence of the light will give a

The Tromso Infant Faces Database (TIF): Development, Validation and Application to Assess Parenting Experience on Clarity and Intensity Ratings.

PubMed

Maack, Jana K; Bohne, Agnes; Nordahl, Dag; Livsdatter, Lina; Lindahl, Åsne A W; Øvervoll, Morten; Wang, Catharina E A; Pfuhl, Gerit

2017-01-01

Newborns and infants are highly depending on successfully communicating their needs; e.g., through crying and facial expressions. Although there is a growing interest in the mechanisms of and possible influences on the recognition of facial expressions in infants, heretofore there exists no validated database of emotional infant faces. In the present article we introduce a standardized and freely available face database containing Caucasian infant face images from 18 infants 4 to 12 months old. The development and validation of the Tromsø Infant Faces (TIF) database is presented in Study 1. Over 700 adults categorized the photographs by seven emotion categories (happy, sad, disgusted, angry, afraid, surprised, neutral) and rated intensity, clarity and their valance. In order to examine the relevance of TIF, we then present its first application in Study 2, investigating differences in emotion recognition across different stages of parenthood. We found a small gender effect in terms of women giving higher intensity and clarity ratings than men. Moreover, parents of young children rated the images as clearer than all the other groups, and parents rated "neutral" expressions as more clearly and more intense. Our results suggest that caretaking experience provides an implicit advantage in the processing of emotional expressions in infant faces, especially for the more difficult, ambiguous expressions.

System for face recognition under expression variations of neutral-sampled individuals using recognized expression warping and a virtual expression-face database

NASA Astrophysics Data System (ADS)

Petpairote, Chayanut; Madarasmi, Suthep; Chamnongthai, Kosin

2018-01-01

The practical identification of individuals using facial recognition techniques requires the matching of faces with specific expressions to faces from a neutral face database. A method for facial recognition under varied expressions against neutral face samples of individuals via recognition of expression warping and the use of a virtual expression-face database is proposed. In this method, facial expressions are recognized and the input expression faces are classified into facial expression groups. To aid facial recognition, the virtual expression-face database is sorted into average facial-expression shapes and by coarse- and fine-featured facial textures. Wrinkle information is also employed in classification by using a process of masking to adjust input faces to match the expression-face database. We evaluate the performance of the proposed method using the CMU multi-PIE, Cohn-Kanade, and AR expression-face databases, and we find that it provides significantly improved results in terms of face recognition accuracy compared to conventional methods and is acceptable for facial recognition under expression variation.

The reference ballistic imaging database revisited.

PubMed

De Ceuster, Jan; Dujardin, Sylvain

2015-03-01

A reference ballistic image database (RBID) contains images of cartridge cases fired in firearms that are in circulation: a ballistic fingerprint database. The performance of an RBID was investigated a decade ago by De Kinder et al. using IBIS(®) Heritage™ technology. The results of that study were published in this journal, issue 214. Since then, technologies have evolved quite significantly and novel apparatus have become available on the market. The current research article investigates the efficiency of another automated ballistic imaging system, Evofinder(®) using the same database as used by De Kinder et al. The results demonstrate a significant increase in correlation efficiency: 38% of all matches were on first position of the Evofinder correlation list in comparison to IBIS(®) Heritage™ where only 19% were on the first position. Average correlation times are comparable to the IBIS(®) Heritage™ system. While Evofinder(®) demonstrates specific improvement for mutually correlating different ammunition brands, ammunition dependence of the markings is still strongly influencing the correlation result because the markings may vary considerably. As a consequence a great deal of potential hits (36%) was still far down in the correlation lists (positions 31 and lower). The large database was used to examine the probability of finding a match as a function of correlation list verification. As an example, the RBID study on Evofinder(®) demonstrates that to find at least 90% of all potential matches, at least 43% of the items in the database need to be compared on screen and this for breech face markings and firing pin impression separately. These results, although a clear improvement to the original RBID study, indicate that the implementation of such a database should still not be considered nowadays. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Multiple Representations-Based Face Sketch-Photo Synthesis.

PubMed

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Tao, Dacheng; Li, Xuelong; Li, Jie

2016-11-01

Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

PubMed

Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli; Rovetta, Alberto; Caleanu, Catalin-Daniel

2015-01-01

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

Development of Human Face Literature Database Using Text Mining Approach: Phase I.

PubMed

Kaur, Paramjit; Krishan, Kewal; Sharma, Suresh K

2018-06-01

The face is an important part of the human body by which an individual communicates in the society. Its importance can be highlighted by the fact that a person deprived of face cannot sustain in the living world. The amount of experiments being performed and the number of research papers being published under the domain of human face have surged in the past few decades. Several scientific disciplines, which are conducting research on human face include: Medical Science, Anthropology, Information Technology (Biometrics, Robotics, and Artificial Intelligence, etc.), Psychology, Forensic Science, Neuroscience, etc. This alarms the need of collecting and managing the data concerning human face so that the public and free access of it can be provided to the scientific community. This can be attained by developing databases and tools on human face using bioinformatics approach. The current research emphasizes on creating a database concerning literature data of human face. The database can be accessed on the basis of specific keywords, journal name, date of publication, author's name, etc. The collected research papers will be stored in the form of a database. Hence, the database will be beneficial to the research community as the comprehensive information dedicated to the human face could be found at one place. The information related to facial morphologic features, facial disorders, facial asymmetry, facial abnormalities, and many other parameters can be extracted from this database. The front end has been developed using Hyper Text Mark-up Language and Cascading Style Sheets. The back end has been developed using hypertext preprocessor (PHP). The JAVA Script has used as scripting language. MySQL (Structured Query Language) is used for database development as it is most widely used Relational Database Management System. XAMPP (X (cross platform), Apache, MySQL, PHP, Perl) open source web application software has been used as the server.The database is still under the

Medical Image Databases

PubMed Central

Tagare, Hemant D.; Jaffe, C. Carl; Duncan, James

1997-01-01

Abstract Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema. PMID:9147338

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

PubMed

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Normative Databases for Imaging Instrumentation.

PubMed

Realini, Tony; Zangwill, Linda M; Flanagan, John G; Garway-Heath, David; Patella, Vincent M; Johnson, Chris A; Artes, Paul H; Gaddie, Ian B; Fingeret, Murray

2015-08-01

To describe the process by which imaging devices undergo reference database development and regulatory clearance. The limitations and potential improvements of reference (normative) data sets for ophthalmic imaging devices will be discussed. A symposium was held in July 2013 in which a series of speakers discussed issues related to the development of reference databases for imaging devices. Automated imaging has become widely accepted and used in glaucoma management. The ability of such instruments to discriminate healthy from glaucomatous optic nerves, and to detect glaucomatous progression over time is limited by the quality of reference databases associated with the available commercial devices. In the absence of standardized rules governing the development of reference databases, each manufacturer's database differs in size, eligibility criteria, and ethnic make-up, among other key features. The process for development of imaging reference databases may be improved by standardizing eligibility requirements and data collection protocols. Such standardization may also improve the degree to which results may be compared between commercial instruments.

Normative Databases for Imaging Instrumentation

PubMed Central

Realini, Tony; Zangwill, Linda; Flanagan, John; Garway-Heath, David; Patella, Vincent Michael; Johnson, Chris; Artes, Paul; Ben Gaddie, I.; Fingeret, Murray

2015-01-01

Purpose To describe the process by which imaging devices undergo reference database development and regulatory clearance. The limitations and potential improvements of reference (normative) data sets for ophthalmic imaging devices will be discussed. Methods A symposium was held in July 2013 in which a series of speakers discussed issues related to the development of reference databases for imaging devices. Results Automated imaging has become widely accepted and used in glaucoma management. The ability of such instruments to discriminate healthy from glaucomatous optic nerves, and to detect glaucomatous progression over time is limited by the quality of reference databases associated with the available commercial devices. In the absence of standardized rules governing the development of reference databases, each manufacturer’s database differs in size, eligibility criteria, and ethnic make-up, among other key features. Conclusions The process for development of imaging reference databases may be improved by standardizing eligibility requirements and data collection protocols. Such standardization may also improve the degree to which results may be compared between commercial instruments. PMID:25265003

Visual cryptography for face privacy

NASA Astrophysics Data System (ADS)

Ross, Arun; Othman, Asem A.

2010-04-01

We discuss the problem of preserving the privacy of a digital face image stored in a central database. In the proposed scheme, a private face image is dithered into two host face images such that it can be revealed only when both host images are simultaneously available; at the same time, the individual host images do not reveal the identity of the original image. In order to accomplish this, we appeal to the field of Visual Cryptography. Experimental results confirm the following: (a) the possibility of hiding a private face image in two unrelated host face images; (b) the successful matching of face images that are reconstructed by superimposing the host images; and (c) the inability of the host images, known as sheets, to reveal the identity of the secret face image.

Face recognition based on symmetrical virtual image and original training image

NASA Astrophysics Data System (ADS)

Ke, Jingcheng; Peng, Yali; Liu, Shigang; Li, Jun; Pei, Zhao

2018-02-01

In face representation-based classification methods, we are able to obtain high recognition rate if a face has enough available training samples. However, in practical applications, we only have limited training samples to use. In order to obtain enough training samples, many methods simultaneously use the original training samples and corresponding virtual samples to strengthen the ability of representing the test sample. One is directly using the original training samples and corresponding mirror samples to recognize the test sample. However, when the test sample is nearly symmetrical while the original training samples are not, the integration of the original training and mirror samples might not well represent the test samples. To tackle the above-mentioned problem, in this paper, we propose a novel method to obtain a kind of virtual samples which are generated by averaging the original training samples and corresponding mirror samples. Then, the original training samples and the virtual samples are integrated to recognize the test sample. Experimental results on five face databases show that the proposed method is able to partly overcome the challenges of the various poses, facial expressions and illuminations of original face image.

Performance evaluation of wavelet-based face verification on a PDA recorded database

NASA Astrophysics Data System (ADS)

Sellahewa, Harin; Jassim, Sabah A.

2006-05-01

The rise of international terrorism and the rapid increase in fraud and identity theft has added urgency to the task of developing biometric-based person identification as a reliable alternative to conventional authentication methods. Human Identification based on face images is a tough challenge in comparison to identification based on fingerprints or Iris recognition. Yet, due to its unobtrusive nature, face recognition is the preferred method of identification for security related applications. The success of such systems will depend on the support of massive infrastructures. Current mobile communication devices (3G smart phones) and PDA's are equipped with a camera which can capture both still and streaming video clips and a touch sensitive display panel. Beside convenience, such devices provide an adequate secure infrastructure for sensitive & financial transactions, by protecting against fraud and repudiation while ensuring accountability. Biometric authentication systems for mobile devices would have obvious advantages in conflict scenarios when communication from beyond enemy lines is essential to save soldier and civilian life. In areas of conflict or disaster the luxury of fixed infrastructure is not available or destroyed. In this paper, we present a wavelet-based face verification scheme that have been specifically designed and implemented on a currently available PDA. We shall report on its performance on the benchmark audio-visual BANCA database and on a newly developed PDA recorded audio-visual database that take include indoor and outdoor recordings.

Fiber pixelated image database

NASA Astrophysics Data System (ADS)

Shinde, Anant; Perinchery, Sandeep Menon; Matham, Murukeshan Vadakke

2016-08-01

Imaging of physically inaccessible parts of the body such as the colon at micron-level resolution is highly important in diagnostic medical imaging. Though flexible endoscopes based on the imaging fiber bundle are used for such diagnostic procedures, their inherent honeycomb-like structure creates fiber pixelation effects. This impedes the observer from perceiving the information from an image captured and hinders the direct use of image processing and machine intelligence techniques on the recorded signal. Significant efforts have been made by researchers in the recent past in the development and implementation of pixelation removal techniques. However, researchers have often used their own set of images without making source data available which subdued their usage and adaptability universally. A database of pixelated images is the current requirement to meet the growing diagnostic needs in the healthcare arena. An innovative fiber pixelated image database is presented, which consists of pixelated images that are synthetically generated and experimentally acquired. Sample space encompasses test patterns of different scales, sizes, and shapes. It is envisaged that this proposed database will alleviate the current limitations associated with relevant research and development and would be of great help for researchers working on comb structure removal algorithms.

Foveation: an alternative method to simultaneously preserve privacy and information in face images

NASA Astrophysics Data System (ADS)

Alonso, Víctor E.; Enríquez-Caldera, Rogerio; Sucar, Luis Enrique

2017-03-01

This paper presents a real-time foveation technique proposed as an alternative method for image obfuscation while simultaneously preserving privacy in face deidentification. Relevance of the proposed technique is discussed through a comparative study of the most common distortions methods in face images and an assessment on performance and effectiveness of privacy protection. All the different techniques presented here are evaluated when they go through a face recognition software. Evaluating the data utility preservation was carried out under gender and facial expression classification. Results on quantifying the tradeoff between privacy protection and image information preservation at different obfuscation levels are presented. Comparative results using the facial expression subset of the FERET database show that the technique achieves a good tradeoff between privacy and awareness with 30% of recognition rate and a classification accuracy as high as 88% obtained from the common figures of merit using the privacy-awareness map.

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

InterFace: A software package for face image warping, averaging, and principal components analysis.

PubMed

Kramer, Robin S S; Jenkins, Rob; Burton, A Mike

2017-12-01

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the "face space" produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.

CHRONIS: an animal chromosome image database.

PubMed

Toyabe, Shin-Ichi; Akazawa, Kouhei; Fukushi, Daisuke; Fukui, Kiichi; Ushiki, Tatsuo

2005-01-01

We have constructed a database system named CHRONIS (CHROmosome and Nano-Information System) to collect images of animal chromosomes and related nanotechnological information. CHRONIS enables rapid sharing of information on chromosome research among cell biologists and researchers in other fields via the Internet. CHRONIS is also intended to serve as a liaison tool for researchers who work in different centers. The image database contains more than 3,000 color microscopic images, including karyotypic images obtained from more than 1,000 species of animals. Researchers can browse the contents of the database using a usual World Wide Web interface in the following URL: http://chromosome.med.niigata-u.ac.jp/chronis/servlet/chronisservlet. The system enables users to input new images into the database, to locate images of interest by keyword searches, and to display the images with detailed information. CHRONIS has a wide range of applications, such as searching for appropriate probes for fluorescent in situ hybridization, comparing various kinds of microscopic images of a single species, and finding researchers working in the same field of interest.

Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior

NASA Astrophysics Data System (ADS)

Hsieh, Cheng-Ta; Huang, Kae-Horng; Lee, Chang-Hsing; Han, Chin-Chuan; Fan, Kuo-Chin

2017-12-01

Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.

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.

Tensor discriminant color space for face recognition.

PubMed

Wang, Su-Jing; Yang, Jian; Zhang, Na; Zhou, Chun-Guang

2011-09-01

Recent research efforts reveal that color may provide useful information for face recognition. For different visual tasks, the choice of a color space is generally different. How can a color space be sought for the specific face recognition problem? To address this problem, this paper represents a color image as a third-order tensor and presents the tensor discriminant color space (TDCS) model. The model can keep the underlying spatial structure of color images. With the definition of n-mode between-class scatter matrices and within-class scatter matrices, TDCS constructs an iterative procedure to obtain one color space transformation matrix and two discriminant projection matrices by maximizing the ratio of these two scatter matrices. The experiments are conducted on two color face databases, AR and Georgia Tech face databases, and the results show that both the performance and the efficiency of the proposed method are better than those of the state-of-the-art color image discriminant model, which involve one color space transformation matrix and one discriminant projection matrix, specifically in a complicated face database with various pose variations.

Not All Faces Are Processed Equally: Evidence for Featural Rather than Holistic Processing of One's Own Face in a Face-Imaging Task

ERIC Educational Resources Information Center

Greenberg, Seth N.; Goshen-Gottstein, Yonatan

2009-01-01

The present work considers the mental imaging of faces, with a focus in own-face imaging. Experiments 1 and 3 demonstrated an own-face disadvantage, with slower generation of mental images of one's own face than of other familiar faces. In contrast, Experiment 2 demonstrated that mental images of facial parts are generated more quickly for one's…

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

High-accuracy and robust face recognition system based on optical parallel correlator using a temporal image sequence

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Kodate, Kashiko

2005-09-01

Face recognition is used in a wide range of security systems, such as monitoring credit card use, searching for individuals with street cameras via Internet and maintaining immigration control. There are still many technical subjects under study. For instance, the number of images that can be stored is limited under the current system, and the rate of recognition must be improved to account for photo shots taken at different angles under various conditions. We implemented a fully automatic Fast Face Recognition Optical Correlator (FARCO) system by using a 1000 frame/s optical parallel correlator designed and assembled by us. Operational speed for the 1: N (i.e. matching a pair of images among N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 seconds, including the pre/post processing. From trial 1: N identification experiments using FARCO, we acquired low error rates of 2.6% False Reject Rate and 1.3% False Accept Rate. By making the most of the high-speed data-processing capability of this system, much more robustness can be achieved for various recognition conditions when large-category data are registered for a single person. We propose a face recognition algorithm for the FARCO while employing a temporal image sequence of moving images. Applying this algorithm to a natural posture, a two times higher recognition rate scored compared with our conventional system. The system has high potential for future use in a variety of purposes such as search for criminal suspects by use of street and airport video cameras, registration of babies at hospitals or handling of an immeasurable number of images in a database.

Three-dimensional face model reproduction method using multiview images

NASA Astrophysics Data System (ADS)

Nagashima, Yoshio; Agawa, Hiroshi; Kishino, Fumio

1991-11-01

This paper describes a method of reproducing three-dimensional face models using multi-view images for a virtual space teleconferencing system that achieves a realistic visual presence for teleconferencing. The goal of this research, as an integral component of a virtual space teleconferencing system, is to generate a three-dimensional face model from facial images, synthesize images of the model virtually viewed from different angles, and with natural shadow to suit the lighting conditions of the virtual space. The proposed method is as follows: first, front and side view images of the human face are taken by TV cameras. The 3D data of facial feature points are obtained from front- and side-views by an image processing technique based on the color, shape, and correlation of face components. Using these 3D data, the prepared base face models, representing typical Japanese male and female faces, are modified to approximate the input facial image. The personal face model, representing the individual character, is then reproduced. Next, an oblique view image is taken by TV camera. The feature points of the oblique view image are extracted using the same image processing technique. A more precise personal model is reproduced by fitting the boundary of the personal face model to the boundary of the oblique view image. The modified boundary of the personal face model is determined by using face direction, namely rotation angle, which is detected based on the extracted feature points. After the 3D model is established, the new images are synthesized by mapping facial texture onto the model.

High precision automated face localization in thermal images: oral cancer dataset as test case

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Raman, S. K.; Mukhopadhyay, S.; Patsa, S.; Anjum, N.; Ray, J. G.

2017-02-01

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

Animal Detection in Natural Images: Effects of Color and Image Database

PubMed Central

Zhu, Weina; Drewes, Jan; Gegenfurtner, Karl R.

2013-01-01

The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used. PMID:24130744

Mobile object retrieval in server-based image databases

NASA Astrophysics Data System (ADS)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

NASA Astrophysics Data System (ADS)

Wijaya, Surya Li; Savvides, Marios; Vijaya Kumar, B. V. K.

2005-02-01

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

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

NONE

2011-02-15

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.more » Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (''nodule{>=}3 mm,''''nodule<3 mm,'' and ''non-nodule{>=}3 mm''). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked ''nodule'' by at least one radiologist. 2669 of these lesions were marked ''nodule{>=}3 mm'' by at least one radiologist, of which 928 (34.7%) received such marks

A robust human face detection algorithm

NASA Astrophysics Data System (ADS)

Raviteja, Thaluru; Karanam, Srikrishna; Yeduguru, Dinesh Reddy V.

2012-01-01

Human face detection plays a vital role in many applications like video surveillance, managing a face image database, human computer interface among others. This paper proposes a robust algorithm for face detection in still color images that works well even in a crowded environment. The algorithm uses conjunction of skin color histogram, morphological processing and geometrical analysis for detecting human faces. To reinforce the accuracy of face detection, we further identify mouth and eye regions to establish the presence/absence of face in a particular region of interest.

Image database for digital hand atlas

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente; Dey, Partha S.; Gertych, Arkadiusz; Pospiech-Kurkowska, Sywia

2003-05-01

Bone age assessment is a procedure frequently performed in pediatric patients to evaluate their growth disorder. A commonly used method is atlas matching by a visual comparison of a hand radiograph with a small reference set of old Greulich-Pyle atlas. We have developed a new digital hand atlas with a large set of clinically normal hand images of diverse ethnic groups. In this paper, we will present our system design and implementation of the digital atlas database to support the computer-aided atlas matching for bone age assessment. The system consists of a hand atlas image database, a computer-aided diagnostic (CAD) software module for image processing and atlas matching, and a Web user interface. Users can use a Web browser to push DICOM images, directly or indirectly from PACS, to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, are then extracted and compared with patterns from the atlas image database to assess the bone age. The digital atlas method built on a large image database and current Internet technology provides an alternative to supplement or replace the traditional one for a quantitative, accurate and cost-effective assessment of bone age.

Personality judgments from everyday images of faces

PubMed Central

Sutherland, Clare A. M.; Rowley, Lauren E.; Amoaku, Unity T.; Daguzan, Ella; Kidd-Rossiter, Kate A.; Maceviciute, Ugne; Young, Andrew W.

2015-01-01

People readily make personality attributions to images of strangers' faces. Here we investigated the basis of these personality attributions as made to everyday, naturalistic face images. In a first study, we used 1000 highly varying “ambient image” face photographs to test the correspondence between personality judgments of the Big Five and dimensions known to underlie a range of facial first impressions: approachability, dominance, and youthful-attractiveness. Interestingly, the facial Big Five judgments were found to separate to some extent: judgments of openness, extraversion, emotional stability, and agreeableness were mainly linked to facial first impressions of approachability, whereas conscientiousness judgments involved a combination of approachability and dominance. In a second study we used average face images to investigate which main cues are used by perceivers to make impressions of the Big Five, by extracting consistent cues to impressions from the large variation in the original images. When forming impressions of strangers from highly varying, naturalistic face photographs, perceivers mainly seem to rely on broad facial cues to approachability, such as smiling. PMID:26579008

Method for the reduction of image content redundancy in large image databases

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

A method of increasing information content for content-based image retrieval (CBIR) systems includes the steps of providing a CBIR database, the database having an index for a plurality of stored digital images using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the images. A visual similarity parameter value is calculated based on a degree of visual similarity between features vectors of an incoming image being considered for entry into the database and feature vectors associated with a most similar of the stored images. Based on said visual similarity parameter value it is determined whether to store or how long to store the feature vectors associated with the incoming image in the database.

Uyghur face recognition method combining 2DDCT with POEM

NASA Astrophysics Data System (ADS)

Yi, Lihamu; Ya, Ermaimaiti

2017-11-01

In this paper, in light of the reduced recognition rate and poor robustness of Uyghur face under illumination and partial occlusion, a Uyghur face recognition method combining Two Dimension Discrete Cosine Transform (2DDCT) with Patterns Oriented Edge Magnitudes (POEM) was proposed. Firstly, the Uyghur face images were divided into 8×8 block matrix, and the Uyghur face images after block processing were converted into frequency-domain status using 2DDCT; secondly, the Uyghur face images were compressed to exclude non-sensitive medium frequency parts and non-high frequency parts, so it can reduce the feature dimensions necessary for the Uyghur face images, and further reduce the amount of computation; thirdly, the corresponding POEM histograms of the Uyghur face images were obtained by calculating the feature quantity of POEM; fourthly, the POEM histograms were cascaded together as the texture histogram of the center feature point to obtain the texture features of the Uyghur face feature points; finally, classification of the training samples was carried out using deep learning algorithm. The simulation experiment results showed that the proposed algorithm further improved the recognition rate of the self-built Uyghur face database, and greatly improved the computing speed of the self-built Uyghur face database, and had strong robustness.

A database for spectral image quality

NASA Astrophysics Data System (ADS)

Le Moan, Steven; George, Sony; Pedersen, Marius; Blahová, Jana; Hardeberg, Jon Yngve

2015-01-01

We introduce a new image database dedicated to multi-/hyperspectral image quality assessment. A total of nine scenes representing pseudo-at surfaces of different materials (textile, wood, skin. . . ) were captured by means of a 160 band hyperspectral system with a spectral range between 410 and 1000nm. Five spectral distortions were designed, applied to the spectral images and subsequently compared in a psychometric experiment, in order to provide a basis for applications such as the evaluation of spectral image difference measures. The database can be downloaded freely from http://www.colourlab.no/cid.

Energy conservation using face detection

NASA Astrophysics Data System (ADS)

Deotale, Nilesh T.; Kalbande, Dhananjay R.; Mishra, Akassh A.

2011-10-01

Computerized Face Detection, is concerned with the difficult task of converting a video signal of a person to written text. It has several applications like face recognition, simultaneous multiple face processing, biometrics, security, video surveillance, human computer interface, image database management, digital cameras use face detection for autofocus, selecting regions of interest in photo slideshows that use a pan-and-scale and The Present Paper deals with energy conservation using face detection. Automating the process to a computer requires the use of various image processing techniques. There are various methods that can be used for Face Detection such as Contour tracking methods, Template matching, Controlled background, Model based, Motion based and color based. Basically, the video of the subject are converted into images are further selected manually for processing. However, several factors like poor illumination, movement of face, viewpoint-dependent Physical appearance, Acquisition geometry, Imaging conditions, Compression artifacts makes Face detection difficult. This paper reports an algorithm for conservation of energy using face detection for various devices. The present paper suggests Energy Conservation can be done by Detecting the Face and reducing the brightness of complete image and then adjusting the brightness of the particular area of an image where the face is located using histogram equalization.

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

PubMed

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

Age-related increase of image-invariance in the fusiform face area.

PubMed

Nordt, Marisa; Semmelmann, Kilian; Genç, Erhan; Weigelt, Sarah

2018-06-01

Face recognition undergoes prolonged development from childhood to adulthood, thereby raising the question which neural underpinnings are driving this development. Here, we address the development of the neural foundation of the ability to recognize a face across naturally varying images. Fourteen children (ages, 7-10) and 14 adults (ages, 20-23) watched images of either the same or different faces in a functional magnetic resonance imaging adaptation paradigm. The same face was either presented in exact image repetitions or in varying images. Additionally, a subset of participants completed a behavioral task, in which they decided if the face in consecutively presented images belonged to the same person. Results revealed age-related increases in neural sensitivity to face identity in the fusiform face area. Importantly, ventral temporal face-selective regions exhibited more image-invariance - as indicated by stronger adaptation for different images of the same person - in adults compared to children. Crucially, the amount of adaptation to face identity across varying images was correlated with the ability to recognize individual faces in different images. These results suggest that the increase of image-invariance in face-selective regions might be related to the development of face recognition skills. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Unified framework for automated iris segmentation using distantly acquired face images.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2012-09-01

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

Adapting Local Features for Face Detection in Thermal Image.

PubMed

Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

2017-11-27

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

Locating faces in color photographs using neural networks

NASA Astrophysics Data System (ADS)

Brown, Joe R.; Talley, Jim

1994-03-01

This paper summarizes a research effort in finding the locations and sizes of faces in color images (photographs, video stills, etc.) if, in fact, faces are presented. Scenarios for using such a system include serving as the means of localizing skin for automatic color balancing during photo processing or it could be used as a front-end in a customs port of energy context for a system which identified persona non grata given a database of known faces. The approach presented here is a hybrid system including: a neural pre-processor, some conventional image processing steps, and a neural classifier as the final face/non-face discriminator. Neither the training (containing 17,655 faces) nor the test (containing 1829 faces) imagery databases were constrained in their content or quality. The results for the pilot system are reported along with a discussion for improving the current system.

LIPS database with LIPService: a microscopic image database of intracellular structures in Arabidopsis guard cells.

PubMed

Higaki, Takumi; Kutsuna, Natsumaro; Hasezawa, Seiichiro

2013-05-16

Intracellular configuration is an important feature of cell status. Recent advances in microscopic imaging techniques allow us to easily obtain a large number of microscopic images of intracellular structures. In this circumstance, automated microscopic image recognition techniques are of extreme importance to future phenomics/visible screening approaches. However, there was no benchmark microscopic image dataset for intracellular organelles in a specified plant cell type. We previously established the Live Images of Plant Stomata (LIPS) database, a publicly available collection of optical-section images of various intracellular structures of plant guard cells, as a model system of environmental signal perception and transduction. Here we report recent updates to the LIPS database and the establishment of a database table, LIPService. We updated the LIPS dataset and established a new interface named LIPService to promote efficient inspection of intracellular structure configurations. Cell nuclei, microtubules, actin microfilaments, mitochondria, chloroplasts, endoplasmic reticulum, peroxisomes, endosomes, Golgi bodies, and vacuoles can be filtered using probe names or morphometric parameters such as stomatal aperture. In addition to the serial optical sectional images of the original LIPS database, new volume-rendering data for easy web browsing of three-dimensional intracellular structures have been released to allow easy inspection of their configurations or relationships with cell status/morphology. We also demonstrated the utility of the new LIPS image database for automated organelle recognition of images from another plant cell image database with image clustering analyses. The updated LIPS database provides a benchmark image dataset for representative intracellular structures in Arabidopsis guard cells. The newly released LIPService allows users to inspect the relationship between organellar three-dimensional configurations and morphometrical parameters.

Emotion-independent face recognition

NASA Astrophysics Data System (ADS)

De Silva, Liyanage C.; Esther, Kho G. P.

2000-12-01

Current face recognition techniques tend to work well when recognizing faces under small variations in lighting, facial expression and pose, but deteriorate under more extreme conditions. In this paper, a face recognition system to recognize faces of known individuals, despite variations in facial expression due to different emotions, is developed. The eigenface approach is used for feature extraction. Classification methods include Euclidean distance, back propagation neural network and generalized regression neural network. These methods yield 100% recognition accuracy when the training database is representative, containing one image representing the peak expression for each emotion of each person apart from the neutral expression. The feature vectors used for comparison in the Euclidean distance method and for training the neural network must be all the feature vectors of the training set. These results are obtained for a face database consisting of only four persons.

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.

Retrieving high-resolution images over the Internet from an anatomical image database

NASA Astrophysics Data System (ADS)

Strupp-Adams, Annette; Henderson, Earl

1999-12-01

The Visible Human Data set is an important contribution to the national collection of anatomical images. To enhance the availability of these images, the National Library of Medicine has supported the design and development of a prototype object-oriented image database which imports, stores, and distributes high resolution anatomical images in both pixel and voxel formats. One of the key database modules is its client-server Internet interface. This Web interface provides a query engine with retrieval access to high-resolution anatomical images that range in size from 100KB for browser viewable rendered images, to 1GB for anatomical structures in voxel file formats. The Web query and retrieval client-server system is composed of applet GUIs, servlets, and RMI application modules which communicate with each other to allow users to query for specific anatomical structures, and retrieve image data as well as associated anatomical images from the database. Selected images can be downloaded individually as single files via HTTP or downloaded in batch-mode over the Internet to the user's machine through an applet that uses Netscape's Object Signing mechanism. The image database uses ObjectDesign's object-oriented DBMS, ObjectStore that has a Java interface. The query and retrieval systems has been tested with a Java-CDE window system, and on the x86 architecture using Windows NT 4.0. This paper describes the Java applet client search engine that queries the database; the Java client module that enables users to view anatomical images online; the Java application server interface to the database which organizes data returned to the user, and its distribution engine that allow users to download image files individually and/or in batch-mode.

Common hyperspectral image database design

NASA Astrophysics Data System (ADS)

Tian, Lixun; Liao, Ningfang; Chai, Ali

2009-11-01

This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method (CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet, visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor, Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search, query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in C#; Finally an application case is presented in agricultural disease detecting area.

A smart technique for attendance system to recognize faces through parallelism

NASA Astrophysics Data System (ADS)

Prabhavathi, B.; Tanuja, V.; Madhu Viswanatham, V.; Rajashekhara Babu, M.

2017-11-01

Major part of recognising a person is face with the help of image processing techniques we can exploit the physical features of a person. In the old approach method that is used in schools and colleges it is there that the professor calls the student name and then the attendance for the students marked. Here in paper want to deviate from the old approach and go with the new approach by using techniques that are there in image processing. In this paper we presenting spontaneous presence for students in classroom. At first classroom image has been in use and after that image is kept in data record. For the images that are stored in the database we apply system algorithm which includes steps such as, histogram classification, noise removal, face detection and face recognition methods. So by using these steps we detect the faces and then compare it with the database. The attendance gets marked automatically if the system recognizes the faces.

Low dose CT image restoration using a database of image patches

NASA Astrophysics Data System (ADS)

Ha, Sungsoo; Mueller, Klaus

2015-01-01

Reducing the radiation dose in CT imaging has become an active research topic and many solutions have been proposed to remove the significant noise and streak artifacts in the reconstructed images. Most of these methods operate within the domain of the image that is subject to restoration. This, however, poses limitations on the extent of filtering possible. We advocate to take into consideration the vast body of external knowledge that exists in the domain of already acquired medical CT images, since after all, this is what radiologists do when they examine these low quality images. We can incorporate this knowledge by creating a database of prior scans, either of the same patient or a diverse corpus of different patients, to assist in the restoration process. Our paper follows up on our previous work that used a database of images. Using images, however, is challenging since it requires tedious and error prone registration and alignment. Our new method eliminates these problems by storing a diverse set of small image patches in conjunction with a localized similarity matching scheme. We also empirically show that it is sufficient to store these patches without anatomical tags since their statistics are sufficiently strong to yield good similarity matches from the database and as a direct effect, produce image restorations of high quality. A final experiment demonstrates that our global database approach can recover image features that are difficult to preserve with conventional denoising approaches.

Kingfisher: a system for remote sensing image database management

NASA Astrophysics Data System (ADS)

Bruzzo, Michele; Giordano, Ferdinando; Dellepiane, Silvana G.

2003-04-01

At present retrieval methods in remote sensing image database are mainly based on spatial-temporal information. The increasing amount of images to be collected by the ground station of earth observing systems emphasizes the need for database management with intelligent data retrieval capabilities. The purpose of the proposed method is to realize a new content based retrieval system for remote sensing images database with an innovative search tool based on image similarity. This methodology is quite innovative for this application, at present many systems exist for photographic images, as for example QBIC and IKONA, but they are not able to extract and describe properly remote image content. The target database is set by an archive of images originated from an X-SAR sensor (spaceborne mission, 1994). The best content descriptors, mainly texture parameters, guarantees high retrieval performances and can be extracted without losses independently of image resolution. The latter property allows DBMS (Database Management System) to process low amount of information, as in the case of quick-look images, improving time performance and memory access without reducing retrieval accuracy. The matching technique has been designed to enable image management (database population and retrieval) independently of dimensions (width and height). Local and global content descriptors are compared, during retrieval phase, with the query image and results seem to be very encouraging.

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.

The BioImage Database Project: organizing multidimensional biological images in an object-relational database.

PubMed

Carazo, J M; Stelzer, E H

1999-01-01

The BioImage Database Project collects and structures multidimensional data sets recorded by various microscopic techniques relevant to modern life sciences. It provides, as precisely as possible, the circumstances in which the sample was prepared and the data were recorded. It grants access to the actual data and maintains links between related data sets. In order to promote the interdisciplinary approach of modern science, it offers a large set of key words, which covers essentially all aspects of microscopy. Nonspecialists can, therefore, access and retrieve significant information recorded and submitted by specialists in other areas. A key issue of the undertaking is to exploit the available technology and to provide a well-defined yet flexible structure for dealing with data. Its pivotal element is, therefore, a modern object relational database that structures the metadata and ameliorates the provision of a complete service. The BioImage database can be accessed through the Internet. Copyright 1999 Academic Press.

Physiology-based face recognition in the thermal infrared spectrum.

PubMed

Buddharaju, Pradeep; Pavlidis, Ioannis T; Tsiamyrtzis, Panagiotis; Bazakos, Mike

2007-04-01

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of

Document image database indexing with pictorial dictionary

NASA Astrophysics Data System (ADS)

Akbari, Mohammad; Azimi, Reza

2010-02-01

In this paper we introduce a new approach for information retrieval from Persian document image database without using Optical Character Recognition (OCR).At first an attribute called subword upper contour label is defined then, a pictorial dictionary is constructed based on this attribute for the subwords. By this approach we address two issues in document image retrieval: keyword spotting and retrieval according to the document similarities. The proposed methods have been evaluated on a Persian document image database. The results have proved the ability of this approach in document image information retrieval.

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

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.

Novel face-detection method under various environments

NASA Astrophysics Data System (ADS)

Jing, Min-Quan; Chen, Ling-Hwei

2009-06-01

We propose a method to detect a face with different poses under various environments. On the basis of skin color information, skin regions are first extracted from an input image. Next, the shoulder part is cut out by using shape information and the head part is then identified as a face candidate. For a face candidate, a set of geometric features is applied to determine if it is a profile face. If not, then a set of eyelike rectangles extracted from the face candidate and the lighting distribution are used to determine if the face candidate is a nonprofile face. Experimental results show that the proposed method is robust under a wide range of lighting conditions, different poses, and races. The detection rate for the HHI face database is 93.68%. For the Champion face database, the detection rate is 95.15%.

The Basel Face Database: A validated set of photographs reflecting systematic differences in Big Two and Big Five personality dimensions

PubMed Central

Schönborn, Sandro; Greifeneder, Rainer; Vetter, Thomas

2018-01-01

Upon a first encounter, individuals spontaneously associate faces with certain personality dimensions. Such first impressions can strongly impact judgments and decisions and may prove highly consequential. Researchers investigating the impact of facial information often rely on (a) real photographs that have been selected to vary on the dimension of interest, (b) morphed photographs, or (c) computer-generated faces (avatars). All three approaches have distinct advantages. Here we present the Basel Face Database, which combines these advantages. In particular, the Basel Face Database consists of real photographs that are subtly, but systematically manipulated to show variations in the perception of the Big Two and the Big Five personality dimensions. To this end, the information specific to each psychological dimension is isolated and modeled in new photographs. Two studies serve as systematic validation of the Basel Face Database. The Basel Face Database opens a new pathway for researchers across psychological disciplines to investigate effects of perceived personality. PMID:29590124

The Basel Face Database: A validated set of photographs reflecting systematic differences in Big Two and Big Five personality dimensions.

PubMed

Walker, Mirella; Schönborn, Sandro; Greifeneder, Rainer; Vetter, Thomas

2018-01-01

Upon a first encounter, individuals spontaneously associate faces with certain personality dimensions. Such first impressions can strongly impact judgments and decisions and may prove highly consequential. Researchers investigating the impact of facial information often rely on (a) real photographs that have been selected to vary on the dimension of interest, (b) morphed photographs, or (c) computer-generated faces (avatars). All three approaches have distinct advantages. Here we present the Basel Face Database, which combines these advantages. In particular, the Basel Face Database consists of real photographs that are subtly, but systematically manipulated to show variations in the perception of the Big Two and the Big Five personality dimensions. To this end, the information specific to each psychological dimension is isolated and modeled in new photographs. Two studies serve as systematic validation of the Basel Face Database. The Basel Face Database opens a new pathway for researchers across psychological disciplines to investigate effects of perceived personality.

Affective attitudes to face images associated with intracerebral EEG source location before face viewing.

PubMed

Pizzagalli, D; Koenig, T; Regard, M; Lehmann, D

1999-01-01

We investigated whether different, personality-related affective attitudes are associated with different brain electric field (EEG) sources before any emotional challenge (stimulus exposure). A 27-channel EEG was recorded in 15 subjects during eyes-closed resting. After recording, subjects rated 32 images of human faces for affective appeal. The subjects in the first (i.e., most negative) and fourth (i.e., most positive) quartile of general affective attitude were further analyzed. The EEG data (mean=25+/-4. 8 s/subject) were subjected to frequency-domain model dipole source analysis (FFT-Dipole-Approximation), resulting in 3-dimensional intracerebral source locations and strengths for the delta-theta, alpha, and beta EEG frequency band, and for the full range (1.5-30 Hz) band. Subjects with negative attitude (compared to those with positive attitude) showed the following source locations: more inferior for all frequency bands, more anterior for the delta-theta band, more posterior and more right for the alpha, beta and 1.5-30 Hz bands. One year later, the subjects were asked to rate the face images again. The rating scores for the same face images were highly correlated for all subjects, and original and retest affective mean attitude was highly correlated across subjects. The present results show that subjects with different affective attitudes to face images had different active, cerebral, neural populations in a task-free condition prior to viewing the images. We conclude that the brain functional state which implements affective attitude towards face images as a personality feature exists without elicitors, as a continuously present, dynamic feature of brain functioning. Copyright 1999 Elsevier Science B.V.

Self- or familiar-face recognition advantage? New insight using ambient images.

PubMed

Bortolon, Catherine; Lorieux, Siméon; Raffard, Stéphane

2018-06-01

Self-face recognition has been widely explored in the past few years. Nevertheless, the current literature relies on the use of standardized photographs which do not represent daily-life face recognition. Therefore, we aim for the first time to evaluate self-face processing in healthy individuals using natural/ambient images which contain variations in the environment and in the face itself. In total, 40 undergraduate and graduate students performed a forced delayed-matching task, including images of one's own face, friend, famous and unknown individuals. For both reaction time and accuracy, results showed that participants were faster and more accurate when matching different images of their own face compared to both famous and unfamiliar faces. Nevertheless, no significant differences were found between self-face and friend-face and between friend-face and famous-face. They were also faster and more accurate when matching friend and famous faces compared to unfamiliar faces. Our results suggest that faster and more accurate responses to self-face might be better explained by a familiarity effect - that is, (1) the result of frequent exposition to one's own image through mirror and photos, (2) a more robust mental representation of one's own face and (3) strong face recognition units as for other familiar faces.

Reflectance from images: a model-based approach for human faces.

PubMed

Fuchs, Martin; Blanz, Volker; Lensch, Hendrik; Seidel, Hans-Peter

2005-01-01

In this paper, we present an image-based framework that acquires the reflectance properties of a human face. A range scan of the face is not required. Based on a morphable face model, the system estimates the 3D shape and establishes point-to-point correspondence across images taken from different viewpoints and across different individuals' faces. This provides a common parameterization of all reconstructed surfaces that can be used to compare and transfer BRDF data between different faces. Shape estimation from images compensates deformations of the face during the measurement process, such as facial expressions. In the common parameterization, regions of homogeneous materials on the face surface can be defined a priori. We apply analytical BRDF models to express the reflectance properties of each region and we estimate their parameters in a least-squares fit from the image data. For each of the surface points, the diffuse component of the BRDF is locally refined, which provides high detail. We present results for multiple analytical BRDF models, rendered at novel orientations and lighting conditions.

Face recognition using tridiagonal matrix enhanced multivariance products representation

NASA Astrophysics Data System (ADS)

Ã-zay, Evrim Korkmaz

2017-01-01

This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.

Embedded wavelet-based face recognition under variable position

NASA Astrophysics Data System (ADS)

Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

2015-02-01

For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

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.

Wavelet optimization for content-based image retrieval in medical databases.

PubMed

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

Modeling human faces with multi-image photogrammetry

NASA Astrophysics Data System (ADS)

D'Apuzzo, Nicola

2002-03-01

Modeling and measurement of the human face have been increasing by importance for various purposes. Laser scanning, coded light range digitizers, image-based approaches and digital stereo photogrammetry are the used methods currently employed in medical applications, computer animation, video surveillance, teleconferencing and virtual reality to produce three dimensional computer models of the human face. Depending on the application, different are the requirements. Ours are primarily high accuracy of the measurement and automation in the process. The method presented in this paper is based on multi-image photogrammetry. The equipment, the method and results achieved with this technique are here depicted. The process is composed of five steps: acquisition of multi-images, calibration of the system, establishment of corresponding points in the images, computation of their 3-D coordinates and generation of a surface model. The images captured by five CCD cameras arranged in front of the subject are digitized by a frame grabber. The complete system is calibrated using a reference object with coded target points, which can be measured fully automatically. To facilitate the establishment of correspondences in the images, texture in the form of random patterns can be projected from two directions onto the face. The multi-image matching process, based on a geometrical constrained least squares matching algorithm, produces a dense set of corresponding points in the five images. Neighborhood filters are then applied on the matching results to remove the errors. After filtering the data, the three-dimensional coordinates of the matched points are computed by forward intersection using the results of the calibration process; the achieved mean accuracy is about 0.2 mm in the sagittal direction and about 0.1 mm in the lateral direction. The last step of data processing is the generation of a surface model from the point cloud and the application of smooth filters. Moreover, a

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Multimedia explorer: image database, image proxy-server and search-engine.

PubMed

Frankewitsch, T; Prokosch, U

1999-01-01

Multimedia plays a major role in medicine. Databases containing images, movies or other types of multimedia objects are increasing in number, especially on the WWW. However, no good retrieval mechanism or search engine currently exists to efficiently track down such multimedia sources in the vast of information provided by the WWW. Secondly, the tools for searching databases are usually not adapted to the properties of images. HTML pages do not allow complex searches. Therefore establishing a more comfortable retrieval involves the use of a higher programming level like JAVA. With this platform independent language it is possible to create extensions to commonly used web browsers. These applets offer a graphical user interface for high level navigation. We implemented a database using JAVA objects as the primary storage container which are then stored by a JAVA controlled ORACLE8 database. Navigation depends on a structured vocabulary enhanced by a semantic network. With this approach multimedia objects can be encapsulated within a logical module for quick data retrieval.

Synthesis and identification of three-dimensional faces from image(s) and three-dimensional generic models

NASA Astrophysics Data System (ADS)

Liu, Zexi; Cohen, Fernand

2017-11-01

We describe an approach for synthesizing a three-dimensional (3-D) face structure from an image or images of a human face taken at a priori unknown poses using gender and ethnicity specific 3-D generic models. The synthesis process starts with a generic model, which is personalized as images of the person become available using preselected landmark points that are tessellated to form a high-resolution triangular mesh. From a single image, two of the three coordinates of the model are reconstructed in accordance with the given image of the person, while the third coordinate is sampled from the generic model, and the appearance is made in accordance with the image. With multiple images, all coordinates and appearance are reconstructed in accordance with the observed images. This method allows for accurate pose estimation as well as face identification in 3-D rendering of a difficult two-dimensional (2-D) face recognition problem into a much simpler 3-D surface matching problem. The estimation of the unknown pose is achieved using the Levenberg-Marquardt optimization process. Encouraging experimental results are obtained in a controlled environment with high-resolution images under a good illumination condition, as well as for images taken in an uncontrolled environment under arbitrary illumination with low-resolution cameras.

Construction of image database for newspapaer articles using CTS

NASA Astrophysics Data System (ADS)

Kamio, Tatsuo

Nihon Keizai Shimbun, Inc. developed a system of making articles' image database automatically by use of CTS (Computer Typesetting System). Besides the articles and the headlines inputted in CTS, it reproduces the image of elements of such as photography and graphs by article in accordance with information of position on the paper. So to speak, computer itself clips the articles out of the newspaper. Image database is accumulated in magnetic file and optical file and is output to the facsimile of users. With diffusion of CTS, newspaper companies which start to have structure of articles database are increased rapidly, the said system is the first attempt to make database automatically. This paper describes the device of CTS which supports this system and outline.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Multimedia explorer: image database, image proxy-server and search-engine.

PubMed Central

Frankewitsch, T.; Prokosch, U.

1999-01-01

Multimedia plays a major role in medicine. Databases containing images, movies or other types of multimedia objects are increasing in number, especially on the WWW. However, no good retrieval mechanism or search engine currently exists to efficiently track down such multimedia sources in the vast of information provided by the WWW. Secondly, the tools for searching databases are usually not adapted to the properties of images. HTML pages do not allow complex searches. Therefore establishing a more comfortable retrieval involves the use of a higher programming level like JAVA. With this platform independent language it is possible to create extensions to commonly used web browsers. These applets offer a graphical user interface for high level navigation. We implemented a database using JAVA objects as the primary storage container which are then stored by a JAVA controlled ORACLE8 database. Navigation depends on a structured vocabulary enhanced by a semantic network. With this approach multimedia objects can be encapsulated within a logical module for quick data retrieval. PMID:10566463

Automatic forensic face recognition from digital images.

PubMed

Peacock, C; Goode, A; Brett, A

2004-01-01

Digital image evidence is now widely available from criminal investigations and surveillance operations, often captured by security and surveillance CCTV. This has resulted in a growing demand from law enforcement agencies for automatic person-recognition based on image data. In forensic science, a fundamental requirement for such automatic face recognition is to evaluate the weight that can justifiably be attached to this recognition evidence in a scientific framework. This paper describes a pilot study carried out by the Forensic Science Service (UK) which explores the use of digital facial images in forensic investigation. For the purpose of the experiment a specific software package was chosen (Image Metrics Optasia). The paper does not describe the techniques used by the software to reach its decision of probabilistic matches to facial images, but accepts the output of the software as though it were a 'black box'. In this way, the paper lays a foundation for how face recognition systems can be compared in a forensic framework. The aim of the paper is to explore how reliably and under what conditions digital facial images can be presented in evidence.

Deep neural network features for horses identity recognition using multiview horses' face pattern

NASA Astrophysics Data System (ADS)

Jarraya, Islem; Ouarda, Wael; Alimi, Adel M.

2017-03-01

To control the state of horses in the born, breeders needs a monitoring system with a surveillance camera that can identify and distinguish between horses. We proposed in [5] a method of horse's identification at a distance using the frontal facial biometric modality. Due to the change of views, the face recognition becomes more difficult. In this paper, the number of images used in our THoDBRL'2015 database (Tunisian Horses DataBase of Regim Lab) is augmented by adding other images of other views. Thus, we used front, right and left profile face's view. Moreover, we suggested an approach for multiview face recognition. First, we proposed to use the Gabor filter for face characterization. Next, due to the augmentation of the number of images, and the large number of Gabor features, we proposed to test the Deep Neural Network with the auto-encoder to obtain the more pertinent features and to reduce the size of features vector. Finally, we performed the proposed approach on our THoDBRL'2015 database and we used the linear SVM for classification.

Partitioning medical image databases for content-based queries on a Grid.

PubMed

Montagnat, J; Breton, V; E Magnin, I

2005-01-01

In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Grids are promising for content-based image retrieval in medical databases.

Using an image-extended relational database to support content-based image retrieval in a PACS.

PubMed

Traina, Caetano; Traina, Agma J M; Araújo, Myrian R B; Bueno, Josiane M; Chino, Fabio J T; Razente, Humberto; Azevedo-Marques, Paulo M

2005-12-01

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

Face antispoofing based on frame difference and multilevel representation

NASA Astrophysics Data System (ADS)

Benlamoudi, Azeddine; Aiadi, Kamal Eddine; Ouafi, Abdelkrim; Samai, Djamel; Oussalah, Mourad

2017-07-01

Due to advances in technology, today's biometric systems become vulnerable to spoof attacks made by fake faces. These attacks occur when an intruder attempts to fool an established face-based recognition system by presenting a fake face (e.g., print photo or replay attacks) in front of the camera instead of the intruder's genuine face. For this purpose, face antispoofing has become a hot topic in face analysis literature, where several applications with antispoofing task have emerged recently. We propose a solution for distinguishing between real faces and fake ones. Our approach is based on extracting features from the difference between successive frames instead of individual frames. We also used a multilevel representation that divides the frame difference into multiple multiblocks. Different texture descriptors (local binary patterns, local phase quantization, and binarized statistical image features) have then been applied to each block. After the feature extraction step, a Fisher score is applied to sort the features in ascending order according to the associated weights. Finally, a support vector machine is used to differentiate between real and fake faces. We tested our approach on three publicly available databases: CASIA Face Antispoofing database, Replay-Attack database, and MSU Mobile Face Spoofing database. The proposed approach outperforms the other state-of-the-art methods in different media and quality metrics.

Open source database of images DEIMOS: extension for large-scale subjective image quality assessment

NASA Astrophysics Data System (ADS)

Vítek, Stanislav

2014-09-01

DEIMOS (Database of Images: Open Source) is an open-source database of images and video sequences for testing, verification and comparison of various image and/or video processing techniques such as compression, reconstruction and enhancement. This paper deals with extension of the database allowing performing large-scale web-based subjective image quality assessment. Extension implements both administrative and client interface. The proposed system is aimed mainly at mobile communication devices, taking into account advantages of HTML5 technology; it means that participants don't need to install any application and assessment could be performed using web browser. The assessment campaign administrator can select images from the large database and then apply rules defined by various test procedure recommendations. The standard test procedures may be fully customized and saved as a template. Alternatively the administrator can define a custom test, using images from the pool and other components, such as evaluating forms and ongoing questionnaires. Image sequence is delivered to the online client, e.g. smartphone or tablet, as a fully automated assessment sequence or viewer can decide on timing of the assessment if required. Environmental data and viewing conditions (e.g. illumination, vibrations, GPS coordinates, etc.), may be collected and subsequently analyzed.

Molecular Imaging and Contrast Agent Database (MICAD): evolution and progress.

PubMed

Chopra, Arvind; Shan, Liang; Eckelman, W C; Leung, Kam; Latterner, Martin; Bryant, Stephen H; Menkens, Anne

2012-02-01

The purpose of writing this review is to showcase the Molecular Imaging and Contrast Agent Database (MICAD; www.micad.nlm.nih.gov ) to students, researchers, and clinical investigators interested in the different aspects of molecular imaging. This database provides freely accessible, current, online scientific information regarding molecular imaging (MI) probes and contrast agents (CA) used for positron emission tomography, single-photon emission computed tomography, magnetic resonance imaging, X-ray/computed tomography, optical imaging and ultrasound imaging. Detailed information on >1,000 agents in MICAD is provided in a chapter format and can be accessed through PubMed. Lists containing >4,250 unique MI probes and CAs published in peer-reviewed journals and agents approved by the United States Food and Drug Administration as well as a comma separated values file summarizing all chapters in the database can be downloaded from the MICAD homepage. Users can search for agents in MICAD on the basis of imaging modality, source of signal/contrast, agent or target category, pre-clinical or clinical studies, and text words. Chapters in MICAD describe the chemical characteristics (structures linked to PubChem), the in vitro and in vivo activities, and other relevant information regarding an imaging agent. All references in the chapters have links to PubMed. A Supplemental Information Section in each chapter is available to share unpublished information regarding an agent. A Guest Author Program is available to facilitate rapid expansion of the database. Members of the imaging community registered with MICAD periodically receive an e-mail announcement (eAnnouncement) that lists new chapters uploaded to the database. Users of MICAD are encouraged to provide feedback, comments, or suggestions for further improvement of the database by writing to the editors at micad@nlm.nih.gov.

Molecular Imaging and Contrast Agent Database (MICAD): Evolution and Progress

PubMed Central

Chopra, Arvind; Shan, Liang; Eckelman, W. C.; Leung, Kam; Latterner, Martin; Bryant, Stephen H.; Menkens, Anne

2011-01-01

The purpose of writing this review is to showcase the Molecular Imaging and Contrast Agent Database (MICAD; www.micad.nlm.nih.gov) to students, researchers and clinical investigators interested in the different aspects of molecular imaging. This database provides freely accessible, current, online scientific information regarding molecular imaging (MI) probes and contrast agents (CA) used for positron emission tomography, single-photon emission computed tomography, magnetic resonance imaging, x-ray/computed tomography, optical imaging and ultrasound imaging. Detailed information on >1000 agents in MICAD is provided in a chapter format and can be accessed through PubMed. Lists containing >4250 unique MI probes and CAs published in peer-reviewed journals and agents approved by the United States Food and Drug Administration (FDA) as well as a CSV file summarizing all chapters in the database can be downloaded from the MICAD homepage. Users can search for agents in MICAD on the basis of imaging modality, source of signal/contrast, agent or target category, preclinical or clinical studies, and text words. Chapters in MICAD describe the chemical characteristics (structures linked to PubChem), the in vitro and in vivo activities and other relevant information regarding an imaging agent. All references in the chapters have links to PubMed. A Supplemental Information Section in each chapter is available to share unpublished information regarding an agent. A Guest Author Program is available to facilitate rapid expansion of the database. Members of the imaging community registered with MICAD periodically receive an e-mail announcement (eAnnouncement) that lists new chapters uploaded to the database. Users of MICAD are encouraged to provide feedback, comments or suggestions for further improvement of the database by writing to the editors at: micad@nlm.nih.gov PMID:21989943

Content-based image retrieval from a database of fracture images

NASA Astrophysics Data System (ADS)

Müller, Henning; Do Hoang, Phuong Anh; Depeursinge, Adrien; Hoffmeyer, Pierre; Stern, Richard; Lovis, Christian; Geissbuhler, Antoine

2007-03-01

This article describes the use of a medical image retrieval system on a database of 16'000 fractures, selected from surgical routine over several years. Image retrieval has been a very active domain of research for several years. It was frequently proposed for the medical domain, but only few running systems were ever tested in clinical routine. For the planning of surgical interventions after fractures, x-ray images play an important role. The fractures are classified according to exact fracture location, plus whether and to which degree the fracture is damaging articulations to see how complicated a reparation will be. Several classification systems for fractures exist and the classification plus the experience of the surgeon lead in the end to the choice of surgical technique (screw, metal plate, ...). This choice is strongly influenced by the experience and knowledge of the surgeons with respect to a certain technique. Goal of this article is to describe a prototype that supplies similar cases to an example to help treatment planning and find the most appropriate technique for a surgical intervention. Our database contains over 16'000 fracture images before and after a surgical intervention. We use an image retrieval system (GNU Image Finding Tool, GIFT) to find cases/images similar to an example case currently under observation. Problems encountered are varying illumination of images as well as strong anatomic differences between patients. Regions of interest are usually small and the retrieval system needs to focus on this region. Results show that GIFT is capable of supplying similar cases, particularly when using relevance feedback, on such a large database. Usual image retrieval is based on a single image as search target but for this application we have to select images by case as similar cases need to be found and not images. A few false positive cases often remain in the results but they can be sorted out quickly by the surgeons. Image retrieval can

Fourier Domain Optical Coherence Tomography With 3D and En Face Imaging of the Punctum and Vertical Canaliculus: A Step Toward Establishing a Normative Database.

PubMed

Kamal, Saurabh; Ali, Mohammad Javed; Ali, Mohammad Hasnat; Naik, Milind N

2016-01-01

To report the features of Fourier domain optical coherence tomography imaging of the normal punctum and vertical canaliculus. Prospective, interventional series of consecutive healthy and asymptomatic adults, who volunteered for optical coherence tomography imaging, were included in the study. Fourier domain optical coherence tomography images of the punctum and vertical canaliculus along with 3D and En face images were captured using the RTVue scanner with a corneal adaptor module and a wide-angled lens. Maximum punctal diameter, mid-canalicular diameter, and vertical canalicular height were calculated. Statistical analysis was performed using Pearson correlation test, and scatter plot matrices were analyzed. A total of 103 puncta of 52 healthy subjects were studied. Although all the images could depict the punctum and vertical canaliculus and all the desired measurements could be obtained, occasional tear debris within the canaliculus was found to be interfering with the imaging. The mean maximum punctal diameter, mid-canalicular diameter, and vertical canalicular height were recorded as 214.71 ± 73 μm, 125.04 ± 60.69 μm, and 890.41 ± 154.76 μm, respectively, with an insignificant correlation between them. The maximum recorded vertical canalicular height in all the cases was far less than the widely reported depth of 2 mm. High-resolution 3D and En face images provided a detailed topography of punctal surface and overview of vertical canaliculus. Fourier domain optical coherence tomography with 3D and En face imaging is a useful noninvasive modality to image the proximal lacrimal system with consistently reproducible high-resolution images. This is likely to help clinicians in the management of proximal lacrimal disorders.

Recognizing Age-Separated Face Images: Humans and Machines

PubMed Central

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. PMID:25474200

Recognizing age-separated face images: humans and machines.

PubMed

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.

Choosing face: The curse of self in profile image selection.

PubMed

White, David; Sutherland, Clare A M; Burton, Amy L

2017-01-01

People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associated with important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions. We model the process of profile image selection by asking participants to indicate the likelihood that images of their own face ("self-selection") and of an unfamiliar face ("other-selection") would be used as profile images on key social networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites. However, contrary to predictions based on people's general expertise in self-presentation, other-selected images conferred more favorable impressions than self-selected images. We conclude that people make suboptimal choices when selecting their own profile pictures, such that self-perception places important limits on facial first impressions formed by others. These results underscore the dynamic nature of person perception in real-world contexts.

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Morphology-based Query for Galaxy Image Databases

NASA Astrophysics Data System (ADS)

Shamir, Lior

2017-02-01

Galaxies of rare morphology are of paramount scientific interest, as they carry important information about the past, present, and future Universe. Once a rare galaxy is identified, studying it more effectively requires a set of galaxies of similar morphology, allowing generalization and statistical analysis that cannot be done when N=1. Databases generated by digital sky surveys can contain a very large number of galaxy images, and therefore once a rare galaxy of interest is identified it is possible that more instances of the same morphology are also present in the database. However, when a researcher identifies a certain galaxy of rare morphology in the database, it is virtually impossible to mine the database manually in the search for galaxies of similar morphology. Here we propose a computer method that can automatically search databases of galaxy images and identify galaxies that are morphologically similar to a certain user-defined query galaxy. That is, the researcher provides an image of a galaxy of interest, and the pattern recognition system automatically returns a list of galaxies that are visually similar to the target galaxy. The algorithm uses a comprehensive set of descriptors, allowing it to support different types of galaxies, and it is not limited to a finite set of known morphologies. While the list of returned galaxies is neither clean nor complete, it contains a far higher frequency of galaxies of the morphology of interest, providing a substantial reduction of the data. Such algorithms can be integrated into data management systems of autonomous digital sky surveys such as the Large Synoptic Survey Telescope (LSST), where the number of galaxies in the database is extremely large. The source code of the method is available at http://vfacstaff.ltu.edu/lshamir/downloads/udat.

Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

NASA Astrophysics Data System (ADS)

Asavaskulkiet, Krissada

2018-04-01

In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

Exploring Human Cognition Using Large Image Databases.

PubMed

Griffiths, Thomas L; Abbott, Joshua T; Hsu, Anne S

2016-07-01

Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories. Copyright © 2016 Cognitive Science Society, Inc.

A comparative study of DIGNET, average, complete, single hierarchical and k-means clustering algorithms in 2D face image recognition

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2014-06-01

The study in this paper belongs to a more general research of discovering facial sub-clusters in different ethnicity face databases. These new sub-clusters along with other metadata (such as race, sex, etc.) lead to a vector for each face in the database where each vector component represents the likelihood of participation of a given face to each cluster. This vector is then used as a feature vector in a human identification and tracking system based on face and other biometrics. The first stage in this system involves a clustering method which evaluates and compares the clustering results of five different clustering algorithms (average, complete, single hierarchical algorithm, k-means and DIGNET), and selects the best strategy for each data collection. In this paper we present the comparative performance of clustering results of DIGNET and four clustering algorithms (average, complete, single hierarchical and k-means) on fabricated 2D and 3D samples, and on actual face images from various databases, using four different standard metrics. These metrics are the silhouette figure, the mean silhouette coefficient, the Hubert test Γ coefficient, and the classification accuracy for each clustering result. The results showed that, in general, DIGNET gives more trustworthy results than the other algorithms when the metrics values are above a specific acceptance threshold. However when the evaluation results metrics have values lower than the acceptance threshold but not too low (too low corresponds to ambiguous results or false results), then it is necessary for the clustering results to be verified by the other algorithms.

Choriocapillaris Imaging Using Multiple En Face Optical Coherence Tomography Angiography Image Averaging.

PubMed

Uji, Akihito; Balasubramanian, Siva; Lei, Jianqin; Baghdasaryan, Elmira; Al-Sheikh, Mayss; Sadda, SriniVas R

2017-11-01

Imaging of the choriocapillaris in vivo is challenging with existing technology. Optical coherence tomography angiography (OCTA), if optimized, could make the imaging less challenging. To investigate multiple en face image averaging on OCTA images of the choriocapillaris. Observational, cross-sectional case series at a referral institutional practice in Los Angeles, California. From the original cohort of 21 healthy individuals, 17 normal eyes of 17 participants were included in the study. The study dates were August to September 2016. All participants underwent OCTA imaging of the macula covering a 3 × 3-mm area using OCTA software (Cirrus 5000 with AngioPlex; Carl Zeiss Meditec). One eye per participant was repeatedly imaged to obtain 9 OCTA cube scan sets. Registration was first performed using superficial capillary plexus images, and this transformation was then applied to the choriocapillaris images. The 9 registered choriocapillaris images were then averaged. Quantitative parameters were measured on binarized OCTA images and compared with the unaveraged OCTA images. Vessel caliber measurement. Seventeen eyes of 17 participants (mean [SD] age, 35.1 [6.0] years; 9 [53%] female; and 9 [53%] of white race/ethnicity) with sufficient image quality were included in this analysis. The single unaveraged images demonstrated a granular appearance, and the vascular pattern was difficult to discern. After averaging, en face choriocapillaris images showed a meshwork appearance. The mean (SD) diameter of the vessels was 22.8 (5.8) µm (range, 9.6-40.2 µm). Compared with the single unaveraged images, the averaged images showed more flow voids (1423 flow voids [95% CI, 967-1909] vs 1254 flow voids [95% CI, 825-1683], P < .001), smaller average size of the flow voids (911 [95% CI, 301-1521] µm2 vs 1364 [95% CI, 645-2083] µm2, P < .001), and greater vessel density (70.7% [95% CI, 61.9%-79.5%] vs 61.9% [95% CI, 56.0%-67.8%], P < .001). The distribution of the

Face-Likeness and Image Variability Drive Responses in Human Face-Selective Ventral Regions

PubMed Central

Davidenko, Nicolas; Remus, David A.; Grill-Spector, Kalanit

2012-01-01

The human ventral visual stream contains regions that respond selectively to faces over objects. However, it is unknown whether responses in these regions correlate with how face-like stimuli appear. Here, we use parameterized face silhouettes to manipulate the perceived face-likeness of stimuli and measure responses in face- and object-selective ventral regions with high-resolution fMRI. We first use “concentric hyper-sphere” (CH) sampling to define face silhouettes at different distances from the prototype face. Observers rate the stimuli as progressively more face-like the closer they are to the prototype face. Paradoxically, responses in both face- and object-selective regions decrease as face-likeness ratings increase. Because CH sampling produces blocks of stimuli whose variability is negatively correlated with face-likeness, this effect may be driven by more adaptation during high face-likeness (low-variability) blocks than during low face-likeness (high-variability) blocks. We tested this hypothesis by measuring responses to matched-variability (MV) blocks of stimuli with similar face-likeness ratings as with CH sampling. Critically, under MV sampling, we find a face-specific effect: responses in face-selective regions gradually increase with perceived face-likeness, but responses in object-selective regions are unchanged. Our studies provide novel evidence that face-selective responses correlate with the perceived face-likeness of stimuli, but this effect is revealed only when image variability is controlled across conditions. Finally, our data show that variability is a powerful factor that drives responses across the ventral stream. This indicates that controlling variability across conditions should be a critical tool in future neuroimaging studies of face and object representation. PMID:21823208

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

Method for secure electronic voting system: face recognition based approach

NASA Astrophysics Data System (ADS)

Alim, M. Affan; Baig, Misbah M.; Mehboob, Shahzain; Naseem, Imran

2017-06-01

In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

An image database management system for conducting CAD research

NASA Astrophysics Data System (ADS)

Gruszauskas, Nicholas; Drukker, Karen; Giger, Maryellen L.

2007-03-01

The development of image databases for CAD research is not a trivial task. The collection and management of images and their related metadata from multiple sources is a time-consuming but necessary process. By standardizing and centralizing the methods in which these data are maintained, one can generate subsets of a larger database that match the specific criteria needed for a particular research project in a quick and efficient manner. A research-oriented management system of this type is highly desirable in a multi-modality CAD research environment. An online, webbased database system for the storage and management of research-specific medical image metadata was designed for use with four modalities of breast imaging: screen-film mammography, full-field digital mammography, breast ultrasound and breast MRI. The system was designed to consolidate data from multiple clinical sources and provide the user with the ability to anonymize the data. Input concerning the type of data to be stored as well as desired searchable parameters was solicited from researchers in each modality. The backbone of the database was created using MySQL. A robust and easy-to-use interface for entering, removing, modifying and searching information in the database was created using HTML and PHP. This standardized system can be accessed using any modern web-browsing software and is fundamental for our various research projects on computer-aided detection, diagnosis, cancer risk assessment, multimodality lesion assessment, and prognosis. Our CAD database system stores large amounts of research-related metadata and successfully generates subsets of cases that match the user's desired search criteria.

Global Binary Continuity for Color Face Detection With Complex Background

NASA Astrophysics Data System (ADS)

Belavadi, Bhaskar; Mahendra Prashanth, K. V.; Joshi, Sujay S.; Suprathik, N.

2017-08-01

In this paper, we propose a method to detect human faces in color images, with complex background. The proposed algorithm makes use of basically two color space models, specifically HSV and YCgCr. The color segmented image is filled uniformly with a single color (binary) and then all unwanted discontinuous lines are removed to get the final image. Experimental results on Caltech database manifests that the purported model is able to accomplish far better segmentation for faces of varying orientations, skin color and background environment.

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

NASA Astrophysics Data System (ADS)

Cui, Chen; Asari, Vijayan K.

2014-03-01

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

ClearedLeavesDB: an online database of cleared plant leaf images

PubMed Central

2014-01-01

Background Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers. Description The Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database. Conclusions We developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface

ClearedLeavesDB: an online database of cleared plant leaf images.

PubMed

Das, Abhiram; Bucksch, Alexander; Price, Charles A; Weitz, Joshua S

2014-03-28

Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers. The Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database. We developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface allows storage of large collections

Image-Based 3D Face Modeling System

NASA Astrophysics Data System (ADS)

Park, In Kyu; Zhang, Hui; Vezhnevets, Vladimir

2005-12-01

This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2[InlineEquation not available: see fulltext.]3 minutes.

Image Display in Local Database Networks

NASA Astrophysics Data System (ADS)

List, James S.; Olson, Frederick R.

1989-05-01

Dearchival of image data in the form of x-ray film provides a major challenge for radiology departments. In highly active referral environments such as tertiary care hospitals, patients may be referred to multiple clinical subspecialists within a very short time. Each clinical subspecialist frequently requires diagnostic image data to complete the diagnosis. This need for image access often interferes with the normal process of film handling and interpretation, subsequently reducing the efficiency of the department. The concept of creating a local image database on individual nursing stations utilizing the AT&T CommView Results Viewing Station (RVS) is being evaluated. Initial physician acceptance has been favorable. Objective measurements of operational productivity enhancements are in progress.

Discriminating Projections for Estimating Face Age in Wild Images

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

Tokola, Ryan A; Bolme, David S; Ricanek, Karl

2014-01-01

We introduce a novel approach to estimating the age of a human from a single uncontrolled image. Current face age estimation algorithms work well in highly controlled images, and some are robust to changes in illumination, but it is usually assumed that images are close to frontal. This bias is clearly seen in the datasets that are commonly used to evaluate age estimation, which either entirely or mostly consist of frontal images. Using pose-specific projections, our algorithm maps image features into a pose-insensitive latent space that is discriminative with respect to age. Age estimation is then performed using a multi-classmore » SVM. We show that our approach outperforms other published results on the Images of Groups dataset, which is the only age-related dataset with a non-trivial number of off-axis face images, and that we are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset. We also experimentally demonstrate that our feature projections introduce insensitivity to pose.« less

Fast hierarchical knowledge-based approach for human face detection in color images

NASA Astrophysics Data System (ADS)

Jiang, Jun; Gong, Jie; Zhang, Guilin; Hu, Ruolan

2001-09-01

This paper presents a fast hierarchical knowledge-based approach for automatically detecting multi-scale upright faces in still color images. The approach consists of three levels. At the highest level, skin-like regions are determinated by skin model, which is based on the color attributes hue and saturation in HSV color space, as well color attributes red and green in normalized color space. In level 2, a new eye model is devised to select human face candidates in segmented skin-like regions. An important feature of the eye model is that it is independent of the scale of human face. So it is possible for finding human faces in different scale with scanning image only once, and it leads to reduction the computation time of face detection greatly. In level 3, a human face mosaic image model, which is consistent with physical structure features of human face well, is applied to judge whether there are face detects in human face candidate regions. This model includes edge and gray rules. Experiment results show that the approach has high robustness and fast speed. It has wide application perspective at human-computer interactions and visual telephone etc.

Face photo-sketch synthesis and recognition.

PubMed

Wang, Xiaogang; Tang, Xiaoou

2009-11-01

In this paper, we propose a novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model. Our system has three components: 1) given a face photo, synthesizing a sketch drawing; 2) given a face sketch drawing, synthesizing a photo; and 3) searching for face photos in the database based on a query sketch drawn by an artist. It has useful applications for both digital entertainment and law enforcement. We assume that faces to be studied are in a frontal pose, with normal lighting and neutral expression, and have no occlusions. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way. Extensive experiments are conducted on a face sketch database including 606 faces, which can be downloaded from our Web site (http://mmlab.ie.cuhk.edu.hk/facesketch.html).

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.

Multimodality medical image database for temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Siadat, Mohammad-Reza; Soltanian-Zadeh, Hamid; Fotouhi, Farshad A.; Elisevich, Kost

2003-05-01

This paper presents the development of a human brain multi-modality database for surgical candidacy determination in temporal lobe epilepsy. The focus of the paper is on content-based image management, navigation and retrieval. Several medical image-processing methods including our newly developed segmentation method are utilized for information extraction/correlation and indexing. The input data includes T1-, T2-Weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data and EEG data analysis. The database can answer queries regarding issues such as the correlation between the attribute X of the entity Y and the outcome of a temporal lobe epilepsy surgery. The entity Y can be a brain anatomical structure such as the hippocampus. The attribute X can be either a functionality feature of the anatomical structure Y, calculated with SPECT modalities, such as signal average, or a volumetric/morphological feature of the entity Y such as volume or average curvature. The outcome of the surgery can be any surgery assessment such as non-verbal Wechsler memory quotient. A determination is made regarding surgical candidacy by analysis of both textual and image data. The current database system suggests a surgical determination for the cases with relatively small hippocampus and high signal intensity average on FLAIR images within the hippocampus. This indication matches the neurosurgeons expectations/observations. Moreover, as the database gets more populated with patient profiles and individual surgical outcomes, using data mining methods one may discover partially invisible correlations between the contents of different modalities of data and the outcome of the surgery.

A data model and database for high-resolution pathology analytical image informatics.

PubMed

Wang, Fusheng; Kong, Jun; Cooper, Lee; Pan, Tony; Kurc, Tahsin; Chen, Wenjin; Sharma, Ashish; Niedermayr, Cristobal; Oh, Tae W; Brat, Daniel; Farris, Alton B; Foran, David J; Saltz, Joel

2011-01-01

The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming

Face Search at Scale.

PubMed

Wang, Dayong; Otto, Charles; Jain, Anil K

2017-06-01

Given the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to search for persons of interest among the billions of shared photos on these websites. Despite significant progress in face recognition, searching a large collection of unconstrained face images remains a difficult problem. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- k most similar faces using features learned by a convolutional neural network. The k retrieved candidates are re-ranked by combining similarities based on deep features and those output by the COTS matcher. We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images. Experimental results demonstrate that while the deep features perform worse than the COTS matcher on a mugshot dataset (93.7 percent versus 98.6 percent TAR@FAR of 0.01 percent), fusing the deep features with the COTS matcher improves the overall performance ( 99.5 percent TAR@FAR of 0.01 percent). This shows that the learned deep features provide complementary information over representations used in state-of-the-art face matchers. On the unconstrained face image benchmarks, the performance of the learned deep features is competitive with reported accuracies. LFW database: 98.20 percent accuracy under the standard protocol and 88.03 percent TAR@FAR of 0.1 percent under the BLUFR protocol; IJB-A benchmark: 51.0 percent TAR@FAR of 0.1 percent (verification), rank 1 retrieval of 82.2 percent (closed-set search), 61.5 percent FNIR@FAR of 1 percent (open-set search). The proposed face search system offers an excellent trade-off between accuracy and scalability on galleries with millions of images. Additionally, in a face search experiment involving

IntraFace.

PubMed

De la Torre, Fernando; Chu, Wen-Sheng; Xiong, Xuehan; Vicente, Francisco; Ding, Xiaoyu; Cohn, Jeffrey

2015-05-01

Within the last 20 years, there has been an increasing interest in the computer vision community in automated facial image analysis algorithms. This has been driven by applications in animation, market research, autonomous-driving, surveillance, and facial editing among others. To date, there exist several commercial packages for specific facial image analysis tasks such as facial expression recognition, facial attribute analysis or face tracking. However, free and easy-to-use software that incorporates all these functionalities is unavailable. This paper presents IntraFace (IF), a publicly-available software package for automated facial feature tracking, head pose estimation, facial attribute recognition, and facial expression analysis from video. In addition, IFincludes a newly develop technique for unsupervised synchrony detection to discover correlated facial behavior between two or more persons, a relatively unexplored problem in facial image analysis. In tests, IF achieved state-of-the-art results for emotion expression and action unit detection in three databases, FERA, CK+ and RU-FACS; measured audience reaction to a talk given by one of the authors; and discovered synchrony for smiling in videos of parent-infant interaction. IF is free of charge for academic use at http://www.humansensing.cs.cmu.edu/intraface/.

Design and deployment of a large brain-image database for clinical and nonclinical research

NASA Astrophysics Data System (ADS)

Yang, Guo Liang; Lim, Choie Cheio Tchoyoson; Banukumar, Narayanaswami; Aziz, Aamer; Hui, Francis; Nowinski, Wieslaw L.

2004-04-01

An efficient database is an essential component of organizing diverse information on image metadata and patient information for research in medical imaging. This paper describes the design, development and deployment of a large database system serving as a brain image repository that can be used across different platforms in various medical researches. It forms the infrastructure that links hospitals and institutions together and shares data among them. The database contains patient-, pathology-, image-, research- and management-specific data. The functionalities of the database system include image uploading, storage, indexing, downloading and sharing as well as database querying and management with security and data anonymization concerns well taken care of. The structure of database is multi-tier client-server architecture with Relational Database Management System, Security Layer, Application Layer and User Interface. Image source adapter has been developed to handle most of the popular image formats. The database has a user interface based on web browsers and is easy to handle. We have used Java programming language for its platform independency and vast function libraries. The brain image database can sort data according to clinically relevant information. This can be effectively used in research from the clinicians" points of view. The database is suitable for validation of algorithms on large population of cases. Medical images for processing could be identified and organized based on information in image metadata. Clinical research in various pathologies can thus be performed with greater efficiency and large image repositories can be managed more effectively. The prototype of the system has been installed in a few hospitals and is working to the satisfaction of the clinicians.

Redesigning photo-ID to improve unfamiliar face matching performance.

PubMed

White, David; Burton, A Mike; Jenkins, Rob; Kemp, Richard I

2014-06-01

Viewers find it difficult to match photos of unfamiliar faces for identity. Despite this, the use of photographic ID is widespread. In this study we ask whether it is possible to improve face matching performance by replacing single photographs on ID documents with multiple photos or an average image of the bearer. In 3 experiments we compare photo-to-photo matching with photo-to-average matching (where the average is formed from multiple photos of the same person) and photo-to-array matching (where the array comprises separate photos of the same person). We consistently find an accuracy advantage for average images and photo arrays over single photos, and show that this improvement is driven by performance in match trials. In the final experiment, we find a benefit of 4-image arrays relative to average images for unfamiliar faces, but not for familiar faces. We propose that conventional photo-ID format can be improved, and discuss this finding in the context of face recognition more generally. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

Ultrahigh speed en face OCT capsule for endoscopic imaging

PubMed Central

Liang, Kaicheng; Traverso, Giovanni; Lee, Hsiang-Chieh; Ahsen, Osman Oguz; Wang, Zhao; Potsaid, Benjamin; Giacomelli, Michael; Jayaraman, Vijaysekhar; Barman, Ross; Cable, Alex; Mashimo, Hiroshi; Langer, Robert; Fujimoto, James G.

2015-01-01

Depth resolved and en face OCT visualization in vivo may have important clinical applications in endoscopy. We demonstrate a high speed, two-dimensional (2D) distal scanning capsule with a micromotor for fast rotary scanning and a pneumatic actuator for precision longitudinal scanning. Longitudinal position measurement and image registration were performed by optical tracking of the pneumatic scanner. The 2D scanning device enables high resolution imaging over a small field of view and is suitable for OCT as well as other scanning microscopies. Large field of view imaging for screening or surveillance applications can also be achieved by proximally pulling back or advancing the capsule while scanning the distal high-speed micromotor. Circumferential en face OCT was demonstrated in living swine at 250 Hz frame rate and 1 MHz A-scan rate using a MEMS tunable VCSEL light source at 1300 nm. Cross-sectional and en face OCT views of the upper and lower gastrointestinal tract were generated with precision distal pneumatic longitudinal actuation as well as proximal manual longitudinal actuation. These devices could enable clinical studies either as an adjunct to endoscopy, attached to an endoscope, or as a swallowed tethered capsule for non-endoscopic imaging without sedation. The combination of ultrahigh speed imaging and distal scanning capsule technology could enable both screening and surveillance applications. PMID:25909001

Ultrahigh speed en face OCT capsule for endoscopic imaging.

PubMed

Liang, Kaicheng; Traverso, Giovanni; Lee, Hsiang-Chieh; Ahsen, Osman Oguz; Wang, Zhao; Potsaid, Benjamin; Giacomelli, Michael; Jayaraman, Vijaysekhar; Barman, Ross; Cable, Alex; Mashimo, Hiroshi; Langer, Robert; Fujimoto, James G

2015-04-01

Depth resolved and en face OCT visualization in vivo may have important clinical applications in endoscopy. We demonstrate a high speed, two-dimensional (2D) distal scanning capsule with a micromotor for fast rotary scanning and a pneumatic actuator for precision longitudinal scanning. Longitudinal position measurement and image registration were performed by optical tracking of the pneumatic scanner. The 2D scanning device enables high resolution imaging over a small field of view and is suitable for OCT as well as other scanning microscopies. Large field of view imaging for screening or surveillance applications can also be achieved by proximally pulling back or advancing the capsule while scanning the distal high-speed micromotor. Circumferential en face OCT was demonstrated in living swine at 250 Hz frame rate and 1 MHz A-scan rate using a MEMS tunable VCSEL light source at 1300 nm. Cross-sectional and en face OCT views of the upper and lower gastrointestinal tract were generated with precision distal pneumatic longitudinal actuation as well as proximal manual longitudinal actuation. These devices could enable clinical studies either as an adjunct to endoscopy, attached to an endoscope, or as a swallowed tethered capsule for non-endoscopic imaging without sedation. The combination of ultrahigh speed imaging and distal scanning capsule technology could enable both screening and surveillance applications.

Age synthesis and estimation via faces: a survey.

PubMed

Fu, Yun; Guo, Guodong; Huang, Thomas S

2010-11-01

Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

A database system to support image algorithm evaluation

NASA Technical Reports Server (NTRS)

Lien, Y. E.

1977-01-01

The design is given of an interactive image database system IMDB, which allows the user to create, retrieve, store, display, and manipulate images through the facility of a high-level, interactive image query (IQ) language. The query language IQ permits the user to define false color functions, pixel value transformations, overlay functions, zoom functions, and windows. The user manipulates the images through generic functions. The user can direct images to display devices for visual and qualitative analysis. Image histograms and pixel value distributions can also be computed to obtain a quantitative analysis of images.

Face recognition using slow feature analysis and contourlet transform

NASA Astrophysics Data System (ADS)

Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

2018-04-01

In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

Recognizing Disguised Faces: Human and Machine Evaluation

PubMed Central

Dhamecha, Tejas Indulal; Singh, Richa; Vatsa, Mayank; Kumar, Ajay

2014-01-01

Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images. PMID:25029188

Image-based query-by-example for big databases of galaxy images

NASA Astrophysics Data System (ADS)

Shamir, Lior; Kuminski, Evan

2017-01-01

Very large astronomical databases containing millions or even billions of galaxy images have been becoming increasingly important tools in astronomy research. However, in many cases the very large size makes it more difficult to analyze these data manually, reinforcing the need for computer algorithms that can automate the data analysis process. An example of such task is the identification of galaxies of a certain morphology of interest. For instance, if a rare galaxy is identified it is reasonable to expect that more galaxies of similar morphology exist in the database, but it is virtually impossible to manually search these databases to identify such galaxies. Here we describe computer vision and pattern recognition methodology that receives a galaxy image as an input, and searches automatically a large dataset of galaxies to return a list of galaxies that are visually similar to the query galaxy. The returned list is not necessarily complete or clean, but it provides a substantial reduction of the original database into a smaller dataset, in which the frequency of objects visually similar to the query galaxy is much higher. Experimental results show that the algorithm can identify rare galaxies such as ring galaxies among datasets of 10,000 astronomical objects.

Imaging assessment of penetrating injury of the neck and face.

PubMed

Offiah, Curtis; Hall, Edward

2012-10-01

Penetrating trauma of the neck and face is a frequent presentation to acute emergency, trauma and critical care units. There remains a steady incidence of both gunshot penetrating injury to the neck and face as well as non-missile penetrating injury-largely, but not solely, knife-related. Optimal imaging assessment of such injuries therefore remains an on-going requirement of the general and specialised radiologist. The anatomy of the neck and face-in particular, vascular, pharyngo-oesophageal, laryngo-tracheal and neural anatomy-demands a more specialised and selective management plan which incorporates specific imaging techniques. The current treatment protocol of injuries of the neck and face has seen a radical shift away from expectant surgical exploration in the management of such injuries, largely as a result of advances in the diagnostic capabilities of multi-detector computed tomography angiography (MDCTA), which is now the first-line imaging modality of choice in such cases. This review aims to highlight ballistic considerations, differing imaging modalities, including MDCTA, that might be utilised to assist in the accurate assessment of these injuries as well as the specific radiological features and patterns of specific organ-system injuries that should be considered and communicated to surgical and critical care teams. TEACHING POINTS : • MDCTA is the first-line imaging modality in penetrating trauma of the neck and, often, of the face • The inherent deformability of a bullet is a significant factor in its tissue-damaging capabilities • MDCTA can provide accurate assessment of visceral injury of the neck as well as vascular injury • Penetrating facial trauma warrants radiological assessment of key adjacent anatomical structures • In-driven fragments of native bone potentiate tissue damage in projectile penetrating facial trauma.

Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a Low-Dimensional Expression Deformation Manifold.

PubMed

Wang, Shu-Fan; Lai, Shang-Hong

2011-10-01

Facial expression modeling is central to facial expression recognition and expression synthesis for facial animation. In this work, we propose a manifold-based 3D face reconstruction approach to estimating the 3D face model and the associated expression deformation from a single face image. With the proposed robust weighted feature map (RWF), we can obtain the dense correspondences between 3D face models and build a nonlinear 3D expression manifold from a large set of 3D facial expression models. Then a Gaussian mixture model in this manifold is learned to represent the distribution of expression deformation. By combining the merits of morphable neutral face model and the low-dimensional expression manifold, a novel algorithm is developed to reconstruct the 3D face geometry as well as the facial deformation from a single face image in an energy minimization framework. Experimental results on simulated and real images are shown to validate the effectiveness and accuracy of the proposed algorithm.

Distributed data collection for a database of radiological image interpretations

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Ostchega, Yechiam; Goh, Gin-Hua; Thoma, George R.

1997-01-01

The National Library of Medicine, in collaboration with the National Center for Health Statistics and the National Institute for Arthritis and Musculoskeletal and Skin Diseases, has built a system for collecting radiological interpretations for a large set of x-ray images acquired as part of the data gathered in the second National Health and Nutrition Examination Survey. This system is capable of delivering across the Internet 5- and 10-megabyte x-ray images to Sun workstations equipped with X Window based 2048 X 2560 image displays, for the purpose of having these images interpreted for the degree of presence of particular osteoarthritic conditions in the cervical and lumbar spines. The collected interpretations can then be stored in a database at the National Library of Medicine, under control of the Illustra DBMS. This system is a client/server database application which integrates (1) distributed server processing of client requests, (2) a customized image transmission method for faster Internet data delivery, (3) distributed client workstations with high resolution displays, image processing functions and an on-line digital atlas, and (4) relational database management of the collected data.

Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images.

PubMed

Sajid, Muhammad; Taj, Imtiaz Ahmad; Bajwa, Usama Ijaz; Ratyal, Naeem Iqbal

2018-04-23

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods. © 2018 American Academy of Forensic Sciences.

A Method for En Face OCT Imaging of Subretinal Fluid in Age-Related Macular Degeneration

PubMed Central

Mohammad, Fatimah; Wanek, Justin; Zelkha, Ruth; Lim, Jennifer I.; Chen, Judy; Shahidi, Mahnaz

2014-01-01

Purpose. The purpose of the study is to report a method for en face imaging of subretinal fluid (SRF) due to age-related macular degeneration (AMD) based on spectral domain optical coherence tomography (SDOCT). Methods. High density SDOCT imaging was performed at two visits in 4 subjects with neovascular AMD and one healthy subject. En face OCT images of a retinal layer anterior to the retinal pigment epithelium were generated. Validity, repeatability, and utility of the method were established. Results. En face OCT images generated by manual and automatic segmentation were nearly indistinguishable and displayed similar regions of SRF. En face OCT images displayed uniform intensities and similar retinal vascular patterns in a healthy subject, while the size and appearance of a hypopigmented fibrotic scar in an AMD subject were similar at 2 visits. In AMD subjects, dark regions on en face OCT images corresponded to reduced or absent light reflectance due to SRF. On en face OCT images, a decrease in SRF areas with treatment was demonstrated and this corresponded with a reduction in the central subfield retinal thickness. Conclusion. En face OCT imaging is a promising tool for visualization and monitoring of SRF area due to disease progression and treatment. PMID:25478209

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Real-time teleophthalmology versus face-to-face consultation: A systematic review.

PubMed

Tan, Irene J; Dobson, Lucy P; Bartnik, Stephen; Muir, Josephine; Turner, Angus W

2017-08-01

Introduction Advances in imaging capabilities and the evolution of real-time teleophthalmology have the potential to provide increased coverage to areas with limited ophthalmology services. However, there is limited research assessing the diagnostic accuracy of face-to-face teleophthalmology consultation. This systematic review aims to determine if real-time teleophthalmology provides comparable accuracy to face-to-face consultation for the diagnosis of common eye health conditions. Methods A search of PubMed, Embase, Medline and Cochrane databases and manual citation review was conducted on 6 February and 7 April 2016. Included studies involved real-time telemedicine in the field of ophthalmology or optometry, and assessed diagnostic accuracy against gold-standard face-to-face consultation. The revised quality assessment of diagnostic accuracy studies (QUADAS-2) tool assessed risk of bias. Results Twelve studies were included, with participants ranging from four to 89 years old. A broad number of conditions were assessed and include corneal and retinal pathologies, strabismus, oculoplastics and post-operative review. Quality assessment identified a high or unclear risk of bias in patient selection (75%) due to an undisclosed recruitment processes. The index test showed high risk of bias in the included studies, due to the varied interpretation and conduct of real-time teleophthalmology methods. Reference standard risk was overall low (75%), as was the risk due to flow and timing (75%). Conclusion In terms of diagnostic accuracy, real-time teleophthalmology was considered superior to face-to-face consultation in one study and comparable in six studies. Store-and-forward image transmission coupled with real-time videoconferencing is a suitable alternative to overcome poor internet transmission speeds.

A complete database for the Einstein imaging proportional counter

NASA Technical Reports Server (NTRS)

Helfand, David J.

1991-01-01

A complete database for the Einstein Imaging Proportional Counter (IPC) was completed. The original data that makes up the archive is described as well as the structure of the database, the Op-Ed analysis system, the technical advances achieved relative to the analysis of (IPC) data, the data products produced, and some uses to which the database has been put by scientists outside Columbia University over the past year.

Database integration of protocol-specific neurological imaging datasets

PubMed Central

Pacurar, Emil E.; Sethi, Sean K.; Habib, Charbel; Laze, Marius O.; Martis-Laze, Rachel; Haacke, E. Mark

2016-01-01

For many years now, Magnetic Resonance Innovations (MR Innovations), a magnetic resonance imaging (MRI) software development, technology, and research company, has been aggregating a multitude of MRI data from different scanning sites through its collaborations and research contracts. The majority of the data has adhered to neuroimaging protocols developed by our group which has helped ensure its quality and consistency. The protocols involved include the study of: traumatic brain injury, extracranial venous imaging for multiple sclerosis and Parkinson's disease, and stroke. The database has proven invaluable in helping to establish disease biomarkers, validate findings across multiple data sets, develop and refine signal processing algorithms, and establish both public and private research collaborations. Myriad Masters and PhD dissertations have been possible thanks to the availability of this database. As an example of a project that cuts across diseases, we have used the data and specialized software to develop new guidelines for detecting cerebral microbleeds. Ultimately, the database has been vital in our ability to provide tools and information for researchers and radiologists in diagnosing their patients, and we encourage collaborations and welcome sharing of similar data in this database. PMID:25959660

Whole-face procedures for recovering facial images from memory.

PubMed

Frowd, Charlie D; Skelton, Faye; Hepton, Gemma; Holden, Laura; Minahil, Simra; Pitchford, Melanie; McIntyre, Alex; Brown, Charity; Hancock, Peter J B

2013-06-01

Research has indicated that traditional methods for accessing facial memories usually yield unidentifiable images. Recent research, however, has made important improvements in this area to the witness interview, method used for constructing the face and recognition of finished composites. Here, we investigated whether three of these improvements would produce even-more recognisable images when used in conjunction with each other. The techniques are holistic in nature: they involve processes which operate on an entire face. Forty participants first inspected an unfamiliar target face. Nominally 24h later, they were interviewed using a standard type of cognitive interview (CI) to recall the appearance of the target, or an enhanced 'holistic' interview where the CI was followed by procedures for focussing on the target's character. Participants then constructed a composite using EvoFIT, a recognition-type system that requires repeatedly selecting items from face arrays, with 'breeding', to 'evolve' a composite. They either saw faces in these arrays with blurred external features, or an enhanced method where these faces were presented with masked external features. Then, further participants attempted to name the composites, first by looking at the face front-on, the normal method, and then for a second time by looking at the face side-on, which research demonstrates facilitates recognition. All techniques improved correct naming on their own, but together promoted highly-recognisable composites with mean naming at 74% correct. The implication is that these techniques, if used together by practitioners, should substantially increase the detection of suspects using this forensic method of person identification. Copyright © 2013 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

An architecture for a brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.

2000-01-01

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

An open access thyroid ultrasound image database

NASA Astrophysics Data System (ADS)

Pedraza, Lina; Vargas, Carlos; Narváez, Fabián.; Durán, Oscar; Muñoz, Emma; Romero, Eduardo

2015-01-01

Computer aided diagnosis systems (CAD) have been developed to assist radiologists in the detection and diagnosis of abnormalities and a large number of pattern recognition techniques have been proposed to obtain a second opinion. Most of these strategies have been evaluated using different datasets making their performance incomparable. In this work, an open access database of thyroid ultrasound images is presented. The dataset consists of a set of B-mode Ultrasound images, including a complete annotation and diagnostic description of suspicious thyroid lesions by expert radiologists. Several types of lesions as thyroiditis, cystic nodules, adenomas and thyroid cancers were included while an accurate lesion delineation is provided in XML format. The diagnostic description of malignant lesions was confirmed by biopsy. The proposed new database is expected to be a resource for the community to assess different CAD systems.

Imaging the eye fundus with real-time en-face spectral domain optical coherence tomography

PubMed Central

Bradu, Adrian; Podoleanu, Adrian Gh.

2014-01-01

Real-time display of processed en-face spectral domain optical coherence tomography (SD-OCT) images is important for diagnosis. However, due to many steps of data processing requirements, such as Fast Fourier transformation (FFT), data re-sampling, spectral shaping, apodization, zero padding, followed by software cut of the 3D volume acquired to produce an en-face slice, conventional high-speed SD-OCT cannot render an en-face OCT image in real time. Recently we demonstrated a Master/Slave (MS)-OCT method that is highly parallelizable, as it provides reflectivity values of points at depth within an A-scan in parallel. This allows direct production of en-face images. In addition, the MS-OCT method does not require data linearization, which further simplifies the processing. The computation in our previous paper was however time consuming. In this paper we present an optimized algorithm that can be used to provide en-face MS-OCT images much quicker. Using such an algorithm we demonstrate around 10 times faster production of sets of en-face OCT images than previously obtained as well as simultaneous real-time display of up to 4 en-face OCT images of 200 × 200 pixels2 from the fovea and the optic nerve of a volunteer. We also demonstrate 3D and B-scan OCT images obtained from sets of MS-OCT C-scans, i.e. with no FFT and no intermediate step of generation of A-scans. PMID:24761303

Retinotopy and attention to the face and house images in the human visual cortex.

PubMed

Wang, Bin; Yan, Tianyi; Ohno, Seiichiro; Kanazawa, Susumu; Wu, Jinglong

2016-06-01

Attentional modulation of the neural activities in human visual areas has been well demonstrated. However, the retinotopic activities that are driven by face and house images and attention to face and house images remain unknown. In the present study, we used images of faces and houses to estimate the retinotopic activities that were driven by both the images and attention to the images, driven by attention to the images, and driven by the images. Generally, our results show that both face and house images produced similar retinotopic activities in visual areas, which were only observed in the attention + stimulus and the attention conditions, but not in the stimulus condition. The fusiform face area (FFA) responded to faces that were presented on the horizontal meridian, whereas parahippocampal place area (PPA) rarely responded to house at any visual field. We further analyzed the amplitudes of the neural responses to the target wedge. In V1, V2, V3, V3A, lateral occipital area 1 (LO-1), and hV4, the neural responses to the attended target wedge were significantly greater than those to the unattended target wedge. However, in LO-2, ventral occipital areas 1 and 2 (VO-1 and VO-2) and FFA and PPA, the differences were not significant. We proposed that these areas likely have large fields of attentional modulation for face and house images and exhibit responses to both the target wedge and the background stimuli. In addition, we proposed that the absence of retinotopic activity in the stimulus condition might imply no perceived difference between the target wedge and the background stimuli.

IntraFace

PubMed Central

De la Torre, Fernando; Chu, Wen-Sheng; Xiong, Xuehan; Vicente, Francisco; Ding, Xiaoyu; Cohn, Jeffrey

2016-01-01

Within the last 20 years, there has been an increasing interest in the computer vision community in automated facial image analysis algorithms. This has been driven by applications in animation, market research, autonomous-driving, surveillance, and facial editing among others. To date, there exist several commercial packages for specific facial image analysis tasks such as facial expression recognition, facial attribute analysis or face tracking. However, free and easy-to-use software that incorporates all these functionalities is unavailable. This paper presents IntraFace (IF), a publicly-available software package for automated facial feature tracking, head pose estimation, facial attribute recognition, and facial expression analysis from video. In addition, IFincludes a newly develop technique for unsupervised synchrony detection to discover correlated facial behavior between two or more persons, a relatively unexplored problem in facial image analysis. In tests, IF achieved state-of-the-art results for emotion expression and action unit detection in three databases, FERA, CK+ and RU-FACS; measured audience reaction to a talk given by one of the authors; and discovered synchrony for smiling in videos of parent-infant interaction. IF is free of charge for academic use at http://www.humansensing.cs.cmu.edu/intraface/. PMID:27346987

Fourier power spectrum characteristics of face photographs: attractiveness perception depends on low-level image properties.

PubMed

Menzel, Claudia; Hayn-Leichsenring, Gregor U; Langner, Oliver; Wiese, Holger; Redies, Christoph

2015-01-01

We investigated whether low-level processed image properties that are shared by natural scenes and artworks - but not veridical face photographs - affect the perception of facial attractiveness and age. Specifically, we considered the slope of the radially averaged Fourier power spectrum in a log-log plot. This slope is a measure of the distribution of special frequency power in an image. Images of natural scenes and artworks possess - compared to face images - a relatively shallow slope (i.e., increased high spatial frequency power). Since aesthetic perception might be based on the efficient processing of images with natural scene statistics, we assumed that the perception of facial attractiveness might also be affected by these properties. We calculated Fourier slope and other beauty-associated measurements in face images and correlated them with ratings of attractiveness and age of the depicted persons (Study 1). We found that Fourier slope - in contrast to the other tested image properties - did not predict attractiveness ratings when we controlled for age. In Study 2A, we overlaid face images with random-phase patterns with different statistics. Patterns with a slope similar to those in natural scenes and artworks resulted in lower attractiveness and higher age ratings. In Studies 2B and 2C, we directly manipulated the Fourier slope of face images and found that images with shallower slopes were rated as more attractive. Additionally, attractiveness of unaltered faces was affected by the Fourier slope of a random-phase background (Study 3). Faces in front of backgrounds with statistics similar to natural scenes and faces were rated as more attractive. We conclude that facial attractiveness ratings are affected by specific image properties. An explanation might be the efficient coding hypothesis.

Fourier Power Spectrum Characteristics of Face Photographs: Attractiveness Perception Depends on Low-Level Image Properties

PubMed Central

Langner, Oliver; Wiese, Holger; Redies, Christoph

2015-01-01

We investigated whether low-level processed image properties that are shared by natural scenes and artworks – but not veridical face photographs – affect the perception of facial attractiveness and age. Specifically, we considered the slope of the radially averaged Fourier power spectrum in a log-log plot. This slope is a measure of the distribution of special frequency power in an image. Images of natural scenes and artworks possess – compared to face images – a relatively shallow slope (i.e., increased high spatial frequency power). Since aesthetic perception might be based on the efficient processing of images with natural scene statistics, we assumed that the perception of facial attractiveness might also be affected by these properties. We calculated Fourier slope and other beauty-associated measurements in face images and correlated them with ratings of attractiveness and age of the depicted persons (Study 1). We found that Fourier slope – in contrast to the other tested image properties – did not predict attractiveness ratings when we controlled for age. In Study 2A, we overlaid face images with random-phase patterns with different statistics. Patterns with a slope similar to those in natural scenes and artworks resulted in lower attractiveness and higher age ratings. In Studies 2B and 2C, we directly manipulated the Fourier slope of face images and found that images with shallower slopes were rated as more attractive. Additionally, attractiveness of unaltered faces was affected by the Fourier slope of a random-phase background (Study 3). Faces in front of backgrounds with statistics similar to natural scenes and faces were rated as more attractive. We conclude that facial attractiveness ratings are affected by specific image properties. An explanation might be the efficient coding hypothesis. PMID:25835539

A User's Applications of Imaging Techniques: The University of Maryland Historic Textile Database.

ERIC Educational Resources Information Center

Anderson, Clarita S.

1991-01-01

Describes the incorporation of textile images into the University of Maryland Historic Textile Database by a computer user rather than a computer expert. Selection of a database management system is discussed, and PICTUREPOWER, a system that integrates photographic quality images with text and numeric information in databases, is described. (three…

Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies

PubMed Central

Fusar-Poli, Paolo; Placentino, Anna; Carletti, Francesco; Landi, Paola; Allen, Paul; Surguladze, Simon; Benedetti, Francesco; Abbamonte, Marta; Gasparotti, Roberto; Barale, Francesco; Perez, Jorge; McGuire, Philip; Politi, Pierluigi

2009-01-01

Background Most of our social interactions involve perception of emotional information from the faces of other people. Furthermore, such emotional processes are thought to be aberrant in a range of clinical disorders, including psychosis and depression. However, the exact neurofunctional maps underlying emotional facial processing are not well defined. Methods Two independent researchers conducted separate comprehensive PubMed (1990 to May 2008) searches to find all functional magnetic resonance imaging (fMRI) studies using a variant of the emotional faces paradigm in healthy participants. The search terms were: “fMRI AND happy faces,” “fMRI AND sad faces,” “fMRI AND fearful faces,” “fMRI AND angry faces,” “fMRI AND disgusted faces” and “fMRI AND neutral faces.” We extracted spatial coordinates and inserted them in an electronic database. We performed activation likelihood estimation analysis for voxel-based meta-analyses. Results Of the originally identified studies, 105 met our inclusion criteria. The overall database consisted of 1785 brain coordinates that yielded an overall sample of 1600 healthy participants. Quantitative voxel-based meta-analysis of brain activation provided neurofunctional maps for 1) main effect of human faces; 2) main effect of emotional valence; and 3) modulatory effect of age, sex, explicit versus implicit processing and magnetic field strength. Processing of emotional faces was associated with increased activation in a number of visual, limbic, temporoparietal and prefrontal areas; the putamen; and the cerebellum. Happy, fearful and sad faces specifically activated the amygdala, whereas angry or disgusted faces had no effect on this brain region. Furthermore, amygdala sensitivity was greater for fearful than for happy or sad faces. Insular activation was selectively reported during processing of disgusted and angry faces. However, insular sensitivity was greater for disgusted than for angry faces. Conversely

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

PubMed

Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

Rating Nasolabial Aesthetics in Unilateral Cleft Lip and Palate Patients: Cropped Versus Full-Face Images.

PubMed

Schwirtz, Roderic M F; Mulder, Frans J; Mosmuller, David G M; Tan, Robin A; Maal, Thomas J; Prahl, Charlotte; de Vet, Henrica C W; Don Griot, J Peter W

2018-05-01

To determine if cropping facial images affects nasolabial aesthetics assessments in unilateral cleft lip patients and to evaluate the effect of facial attractiveness on nasolabial evaluation. Two cleft surgeons and one cleft orthodontist assessed standardized frontal photographs 4 times; nasolabial aesthetics were rated on cropped and full-face images using the Cleft Aesthetic Rating Scale, and total facial attractiveness was rated on full-face images with and without the nasolabial area blurred using a 5-point Likert scale. Cleft Palate Craniofacial Unit of a University Medical Center. Inclusion criteria: nonsyndromic unilateral cleft lip and an available frontal view photograph around 10 years of age. a history of facial trauma and an incomplete cleft. Eighty-one photographs were available for assessment. Differences in mean CARS scores between cropped versus full-face photographs and attractive versus unattractive rated patients were evaluated by paired t test. Nasolabial aesthetics are scored more negatively on full-face photographs compared to cropped photographs, regardless of facial attractiveness. (Mean CARS score, nose: cropped = 2.8, full-face = 3.0, P < .001; lip: cropped = 2.4, full-face = 2.7, P < .001; nose and lip: cropped = 2.6, full-face = 2.8, P < .001). Aesthetic outcomes of the nasolabial area are assessed significantly more positively when using cropped images compared to full-face images. For this reason, cropping images, revealing the nasolabial area only, is recommended for aesthetical assessments.

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

PubMed

Xiao, Shijie; Xu, Dong; Wu, Jianxin

2015-10-01

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

The depth estimation of 3D face from single 2D picture based on manifold learning constraints

NASA Astrophysics Data System (ADS)

Li, Xia; Yang, Yang; Xiong, Hailiang; Liu, Yunxia

2018-04-01

The estimation of depth is virtual important in 3D face reconstruction. In this paper, we propose a t-SNE based on manifold learning constraints and introduce K-means method to divide the original database into several subset, and the selected optimal subset to reconstruct the 3D face depth information can greatly reduce the computational complexity. Firstly, we carry out the t-SNE operation to reduce the key feature points in each 3D face model from 1×249 to 1×2. Secondly, the K-means method is applied to divide the training 3D database into several subset. Thirdly, the Euclidean distance between the 83 feature points of the image to be estimated and the feature point information before the dimension reduction of each cluster center is calculated. The category of the image to be estimated is judged according to the minimum Euclidean distance. Finally, the method Kong D will be applied only in the optimal subset to estimate the depth value information of 83 feature points of 2D face images. Achieving the final depth estimation results, thus the computational complexity is greatly reduced. Compared with the traditional traversal search estimation method, although the proposed method error rate is reduced by 0.49, the number of searches decreases with the change of the category. In order to validate our approach, we use a public database to mimic the task of estimating the depth of face images from 2D images. The average number of searches decreased by 83.19%.

Video face recognition against a watch list

NASA Astrophysics Data System (ADS)

Abbas, Jehanzeb; Dagli, Charlie K.; Huang, Thomas S.

2007-10-01

Due to a large increase in the video surveillance data recently in an effort to maintain high security at public places, we need more robust systems to analyze this data and make tasks like face recognition a realistic possibility in challenging environments. In this paper we explore a watch-list scenario where we use an appearance based model to classify query faces from low resolution videos into either a watch-list or a non-watch-list face. We then use our simple yet a powerful face recognition system to recognize the faces classified as watch-list faces. Where the watch-list includes those people that we are interested in recognizing. Our system uses simple feature machine algorithms from our previous work to match video faces against still images. To test our approach, we match video faces against a large database of still images obtained from a previous work in the field from Yahoo News over a period of time. We do this matching in an efficient manner to come up with a faster and nearly real-time system. This system can be incorporated into a larger surveillance system equipped with advanced algorithms involving anomalous event detection and activity recognition. This is a step towards more secure and robust surveillance systems and efficient video data analysis.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Image Engine: an object-oriented multimedia database for storing, retrieving and sharing medical images and text.

PubMed Central

Lowe, H. J.

1993-01-01

This paper describes Image Engine, an object-oriented, microcomputer-based, multimedia database designed to facilitate the storage and retrieval of digitized biomedical still images, video, and text using inexpensive desktop computers. The current prototype runs on Apple Macintosh computers and allows network database access via peer to peer file sharing protocols. Image Engine supports both free text and controlled vocabulary indexing of multimedia objects. The latter is implemented using the TView thesaurus model developed by the author. The current prototype of Image Engine uses the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary (with UMLS Meta-1 extensions) as its indexing thesaurus. PMID:8130596

An online database for plant image analysis software tools.

PubMed

Lobet, Guillaume; Draye, Xavier; Périlleux, Claire

2013-10-09

Recent years have seen an increase in methods for plant phenotyping using image analyses. These methods require new software solutions for data extraction and treatment. These solutions are instrumental in supporting various research pipelines, ranging from the localisation of cellular compounds to the quantification of tree canopies. However, due to the variety of existing tools and the lack of central repository, it is challenging for researchers to identify the software that is best suited for their research. We present an online, manually curated, database referencing more than 90 plant image analysis software solutions. The website, plant-image-analysis.org, presents each software in a uniform and concise manner enabling users to identify the available solutions for their experimental needs. The website also enables user feedback, evaluations and new software submissions. The plant-image-analysis.org database provides an overview of existing plant image analysis software. The aim of such a toolbox is to help users to find solutions, and to provide developers a way to exchange and communicate about their work.

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.

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Correlation based efficient face recognition and color change detection

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Alam, M. S.; Qasmi, S.

2013-01-01

Identifying the human face via correlation is a topic attracting widespread interest. At the heart of this technique lies the comparison of an unknown target image to a known reference database of images. However, the color information in the target image remains notoriously difficult to interpret. In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding). We adopt the Vanderlugt correlator (VLC) architecture with a segmented phase filter and we decompose the color target image using normalized red, green, and blue (RGB), and hue, saturation, and value (HSV) scales. We propose a new strategy to effectively utilize color information in signatures for further increasing the discrimination ability. The proposed algorithm has been found to be very efficient for discriminating face subjects with different skin colors, and those having color stains in different areas of the facial image.

Sub-component modeling for face image reconstruction in video communications

NASA Astrophysics Data System (ADS)

Shiell, Derek J.; Xiao, Jing; Katsaggelos, Aggelos K.

2008-08-01

Emerging communications trends point to streaming video as a new form of content delivery. These systems are implemented over wired systems, such as cable or ethernet, and wireless networks, cell phones, and portable game systems. These communications systems require sophisticated methods of compression and error-resilience encoding to enable communications across band-limited and noisy delivery channels. Additionally, the transmitted video data must be of high enough quality to ensure a satisfactory end-user experience. Traditionally, video compression makes use of temporal and spatial coherence to reduce the information required to represent an image. In many communications systems, the communications channel is characterized by a probabilistic model which describes the capacity or fidelity of the channel. The implication is that information is lost or distorted in the channel, and requires concealment on the receiving end. We demonstrate a generative model based transmission scheme to compress human face images in video, which has the advantages of a potentially higher compression ratio, while maintaining robustness to errors and data corruption. This is accomplished by training an offline face model and using the model to reconstruct face images on the receiving end. We propose a sub-component AAM modeling the appearance of sub-facial components individually, and show face reconstruction results under different types of video degradation using a weighted and non-weighted version of the sub-component AAM.

The roles of perceptual and conceptual information in face recognition.

PubMed

Schwartz, Linoy; Yovel, Galit

2016-11-01

The representation of familiar objects is comprised of perceptual information about their visual properties as well as the conceptual knowledge that we have about them. What is the relative contribution of perceptual and conceptual information to object recognition? Here, we examined this question by designing a face familiarization protocol during which participants were either exposed to rich perceptual information (viewing each face in different angles and illuminations) or with conceptual information (associating each face with a different name). Both conditions were compared with single-view faces presented with no labels. Recognition was tested on new images of the same identities to assess whether learning generated a view-invariant representation. Results showed better recognition of novel images of the learned identities following association of a face with a name label, but no enhancement following exposure to multiple face views. Whereas these findings may be consistent with the role of category learning in object recognition, face recognition was better for labeled faces only when faces were associated with person-related labels (name, occupation), but not with person-unrelated labels (object names or symbols). These findings suggest that association of meaningful conceptual information with an image shifts its representation from an image-based percept to a view-invariant concept. They further indicate that the role of conceptual information should be considered to account for the superior recognition that we have for familiar faces and objects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

Applied learning-based color tone mapping for face recognition in video surveillance system

NASA Astrophysics Data System (ADS)

Yew, Chuu Tian; Suandi, Shahrel Azmin

2012-04-01

In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

Face recognition in capuchin monkeys (Cebus apella).

PubMed

Pokorny, Jennifer J; de Waal, Frans B M

2009-05-01

Primates live in complex social groups that necessitate recognition of the individuals with whom they interact. In humans, faces provide a visual means by which to gain information such as identity, allowing us to distinguish between both familiar and unfamiliar individuals. The current study used a computerized oddity task to investigate whether a New World primate, Cebus apella, can discriminate the faces of In-group and Out-group conspecifics based on identity. The current study, improved on past methodologies, demonstrates that capuchins recognize the faces of both familiar and unfamiliar conspecifics. Once a performance criterion had been reached, subjects successfully transferred to a large number of novel images within the first 100 trials thus ruling out performance based on previous conditioning. Capuchins can be added to a growing list of primates that appear to recognize two-dimensional facial images of conspecifics. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Log-Gabor Weber descriptor for face recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Sang, Nong; Gao, Changxin

2015-09-01

The Log-Gabor transform, which is suitable for analyzing gradually changing data such as in iris and face images, has been widely used in image processing, pattern recognition, and computer vision. In most cases, only the magnitude or phase information of the Log-Gabor transform is considered. However, the complementary effect taken by combining magnitude and phase information simultaneously for an image-feature extraction problem has not been systematically explored in the existing works. We propose a local image descriptor for face recognition, called Log-Gabor Weber descriptor (LGWD). The novelty of our LGWD is twofold: (1) to fully utilize the information from the magnitude or phase feature of multiscale and orientation Log-Gabor transform, we apply the Weber local binary pattern operator to each transform response. (2) The encoded Log-Gabor magnitude and phase information are fused at the feature level by utilizing kernel canonical correlation analysis strategy, considering that feature level information fusion is effective when the modalities are correlated. Experimental results on the AR, Extended Yale B, and UMIST face databases, compared with those available from recent experiments reported in the literature, show that our descriptor yields a better performance than state-of-the art methods.

A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data

High-resolution face verification using pore-scale facial features.

PubMed

Li, Dong; Zhou, Huiling; Lam, Kin-Man

2015-08-01

Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT)-adapted from PCA-SIFT-is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

NASA Technical Reports Server (NTRS)

Roussopoulos, N.; Sellis, Timos

1992-01-01

One of biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental database access method, VIEWCACHE, provides such an interface for accessing distributed data sets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image data sets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate distributed database search.

Building a medical image processing algorithm verification database

NASA Astrophysics Data System (ADS)

Brown, C. Wayne

2000-06-01

The design of a database containing head Computed Tomography (CT) studies is presented, along with a justification for the database's composition. The database will be used to validate software algorithms that screen normal head CT studies from studies that contain pathology. The database is designed to have the following major properties: (1) a size sufficient for statistical viability, (2) inclusion of both normal (no pathology) and abnormal scans, (3) inclusion of scans due to equipment malfunction, technologist error, and uncooperative patients, (4) inclusion of data sets from multiple scanner manufacturers, (5) inclusion of data sets from different gender and age groups, and (6) three independent diagnosis of each data set. Designed correctly, the database will provide a partial basis for FDA (United States Food and Drug Administration) approval of image processing algorithms for clinical use. Our goal for the database is the proof of viability of screening head CT's for normal anatomy using computer algorithms. To put this work into context, a classification scheme for 'computer aided diagnosis' systems is proposed.

Appropriateness of the food-pics image database for experimental eating and appetite research with adolescents.

PubMed

Jensen, Chad D; Duraccio, Kara M; Barnett, Kimberly A; Stevens, Kimberly S

2016-12-01

Research examining effects of visual food cues on appetite-related brain processes and eating behavior has proliferated. Recently investigators have developed food image databases for use across experimental studies examining appetite and eating behavior. The food-pics image database represents a standardized, freely available image library originally validated in a large sample primarily comprised of adults. The suitability of the images for use with adolescents has not been investigated. The aim of the present study was to evaluate the appropriateness of the food-pics image library for appetite and eating research with adolescents. Three hundred and seven adolescents (ages 12-17) provided ratings of recognizability, palatability, and desire to eat, for images from the food-pics database. Moreover, participants rated the caloric content (high vs. low) and healthiness (healthy vs. unhealthy) of each image. Adolescents rated approximately 75% of the food images as recognizable. Approximately 65% of recognizable images were correctly categorized as high vs. low calorie and 63% were correctly classified as healthy vs. unhealthy in 80% or more of image ratings. These results suggest that a smaller subset of the food-pics image database is appropriate for use with adolescents. With some modifications to included images, the food-pics image database appears to be appropriate for use in experimental appetite and eating-related research conducted with adolescents. Copyright © 2016 Elsevier Ltd. All rights reserved.

a Review on State-Of Face Recognition Approaches

NASA Astrophysics Data System (ADS)

Mahmood, Zahid; Muhammad, Nazeer; Bibi, Nargis; Ali, Tauseef

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

NASA Technical Reports Server (NTRS)

Roussopoulos, N.; Sellis, Timos

1993-01-01

One of the biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental data base access method, VIEWCACHE, provides such an interface for accessing distributed datasets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image datasets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate database search.

Automatic Detection of Frontal Face Midline by Chain-coded Merlin-Farber Hough Trasform

NASA Astrophysics Data System (ADS)

Okamoto, Daichi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka

We propose a novel approach for detection of the facial midline (facial symmetry axis) from a frontal face image. The facial midline has several applications, for instance reducing computational cost required for facial feature extraction (FFE) and postoperative assessment for cosmetic or dental surgery. The proposed method detects the facial midline of a frontal face from an edge image as the symmetry axis using the Merlin-Faber Hough transformation. And a new performance improvement scheme for midline detection by MFHT is present. The main concept of the proposed scheme is suppression of redundant vote on the Hough parameter space by introducing chain code representation for the binary edge image. Experimental results on the image dataset containing 2409 images from FERET database indicate that the proposed algorithm can improve the accuracy of midline detection from 89.9% to 95.1 % for face images with different scales and rotation.

Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

NASA Astrophysics Data System (ADS)

Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

Low-level image properties in facial expressions.

PubMed

Menzel, Claudia; Redies, Christoph; Hayn-Leichsenring, Gregor U

2018-06-04

We studied low-level image properties of face photographs and analyzed whether they change with different emotional expressions displayed by an individual. Differences in image properties were measured in three databases that depicted a total of 167 individuals. Face images were used either in their original form, cut to a standard format or superimposed with a mask. Image properties analyzed were: brightness, redness, yellowness, contrast, spectral slope, overall power and relative power in low, medium and high spatial frequencies. Results showed that image properties differed significantly between expressions within each individual image set. Further, specific facial expressions corresponded to patterns of image properties that were consistent across all three databases. In order to experimentally validate our findings, we equalized the luminance histograms and spectral slopes of three images from a given individual who showed two expressions. Participants were significantly slower in matching the expression in an equalized compared to an original image triad. Thus, existing differences in these image properties (i.e., spectral slope, brightness or contrast) facilitate emotion detection in particular sets of face images. Copyright © 2018. Published by Elsevier B.V.

Combination of Face Regions in Forensic Scenarios.

PubMed

Tome, Pedro; Fierrez, Julian; Vera-Rodriguez, Ruben; Ortega-Garcia, Javier

2015-07-01

This article presents an experimental analysis of the combination of different regions of the human face on various forensic scenarios to generate scientific knowledge useful for the forensic experts. Three scenarios of interest at different distances are considered comparing mugshot and CCTV face images using MORPH and SC face databases. One of the main findings is that inner facial regions combine better in mugshot and close CCTV scenarios and outer facial regions combine better in far CCTV scenarios. This means, that depending of the acquisition distance, the discriminative power of the facial regions change, having in some cases better performance than the full face. This effect can be exploited by considering the fusion of facial regions which results in a very significant improvement of the discriminative performance compared to just using the full face. © 2015 American Academy of Forensic Sciences.

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

PubMed

Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Image Databases.

ERIC Educational Resources Information Center

Pettersson, Rune

Different kinds of pictorial databases are described with respect to aims, user groups, search possibilities, storage, and distribution. Some specific examples are given for databases used for the following purposes: (1) labor markets for artists; (2) document management; (3) telling a story; (4) preservation (archives and museums); (5) research;…

A computer-generated animated face stimulus set for psychophysiological research

PubMed Central

Naples, Adam; Nguyen-Phuc, Alyssa; Coffman, Marika; Kresse, Anna; Faja, Susan; Bernier, Raphael; McPartland., James

2014-01-01

Human faces are fundamentally dynamic, but experimental investigations of face perception traditionally rely on static images of faces. While naturalistic videos of actors have been used with success in some contexts, much research in neuroscience and psychophysics demands carefully controlled stimuli. In this paper, we describe a novel set of computer generated, dynamic, face stimuli. These grayscale faces are tightly controlled for low- and high-level visual properties. All faces are standardized in terms of size, luminance, and location and size of facial features. Each face begins with a neutral pose and transitions to an expression over the course of 30 frames. Altogether there are 222 stimuli spanning 3 different categories of movement: (1) an affective movement (fearful face); (2) a neutral movement (close-lipped, puffed cheeks with open eyes); and (3) a biologically impossible movement (upward dislocation of eyes and mouth). To determine whether early brain responses sensitive to low-level visual features differed between expressions, we measured the occipital P100 event related potential (ERP), which is known to reflect differences in early stages of visual processing and the N170, which reflects structural encoding of faces. We found no differences between faces at the P100, indicating that different face categories were well matched on low-level image properties. This database provides researchers with a well-controlled set of dynamic faces controlled on low-level image characteristics that are applicable to a range of research questions in social perception. PMID:25028164

Optical correlators for recognition of human face thermal images

NASA Astrophysics Data System (ADS)

Bauer, Joanna; Podbielska, Halina; Suchwalko, Artur; Mazurkiewicz, Jacek

2005-09-01

In this paper, the application of the optical correlators for face thermograms recognition is described. The thermograms were colleted from 27 individuals. For each person 10 pictures in different conditions were recorded and the data base composed of 270 images was prepared. Two biometric systems based on joint transform correlator and 4f correlator were built. Each system was designed for realizing two various tasks: verification and identification. The recognition systems were tested and evaluated according to the Face Recognition Vendor Tests (FRVT).

NOAA Data Rescue of Key Solar Databases and Digitization of Historical Solar Images

NASA Astrophysics Data System (ADS)

Coffey, H. E.

2006-08-01

Over a number of years, the staff at NOAA National Geophysical Data Center (NGDC) has worked to rescue key solar databases by converting them to digital format and making them available via the World Wide Web. NOAA has had several data rescue programs where staff compete for funds to rescue important and critical historical data that are languishing in archives and at risk of being lost due to deteriorating condition, loss of any metadata or descriptive text that describe the databases, lack of interest or funding in maintaining databases, etc. The Solar-Terrestrial Physics Division at NGDC was able to obtain funds to key in some critical historical tabular databases. Recently the NOAA Climate Database Modernization Program (CDMP) funded a project to digitize historical solar images, producing a large online database of historical daily full disk solar images. The images include the wavelengths Calcium K, Hydrogen Alpha, and white light photos, as well as sunspot drawings and the comprehensive drawings of a multitude of solar phenomena on one daily map (Fraunhofer maps and Wendelstein drawings). Included in the digitization are high resolution solar H-alpha images taken at the Boulder Solar Observatory 1967-1984. The scanned daily images document many phases of solar activity, from decadal variation to rotational variation to daily changes. Smaller versions are available online. Larger versions are available by request. See http://www.ngdc.noaa.gov/stp/SOLAR/ftpsolarimages.html. The tabular listings and solar imagery will be discussed.

Characterization and recognition of mixed emotional expressions in thermal face image

NASA Astrophysics Data System (ADS)

Saha, Priya; Bhattacharjee, Debotosh; De, Barin K.; Nasipuri, Mita

2016-05-01

Facial expressions in infrared imaging have been introduced to solve the problem of illumination, which is an integral constituent of visual imagery. The paper investigates facial skin temperature distribution on mixed thermal facial expressions of our created face database where six are basic expressions and rest 12 are a mixture of those basic expressions. Temperature analysis has been performed on three facial regions of interest (ROIs); periorbital, supraorbital and mouth. Temperature variability of the ROIs in different expressions has been measured using statistical parameters. The temperature variation measurement in ROIs of a particular expression corresponds to a vector, which is later used in recognition of mixed facial expressions. Investigations show that facial features in mixed facial expressions can be characterized by positive emotion induced facial features and negative emotion induced facial features. Supraorbital is a useful facial region that can differentiate basic expressions from mixed expressions. Analysis and interpretation of mixed expressions have been conducted with the help of box and whisker plot. Facial region containing mixture of two expressions is generally less temperature inducing than corresponding facial region containing basic expressions.

Face Liveness Detection Using Defocus

PubMed Central

Kim, Sooyeon; Ban, Yuseok; Lee, Sangyoun

2015-01-01

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones. PMID:25594594

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics.

PubMed

Mano, Shoji; Miwa, Tomoki; Nishikawa, Shuh-ichi; Mimura, Tetsuro; Nishimura, Mikio

2009-12-01

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

Assessing paedophilia based on the haemodynamic brain response to face images.

PubMed

Ponseti, Jorge; Granert, Oliver; Van Eimeren, Thilo; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Deuschl, Günther; Huchzermeier, Christian; Stirn, Aglaja; Bosinski, Hartmut; Roman Siebner, Hartwig

2016-01-01

Objective assessment of sexual preferences may be of relevance in the treatment and prognosis of child sexual offenders. Previous research has indicated that this can be achieved by pattern classification of brain responses to sexual child and adult images. Our recent research showed that human face processing is tuned to sexual age preferences. This observation prompted us to test whether paedophilia can be inferred based on the haemodynamic brain responses to adult and child faces. Twenty-four men sexually attracted to prepubescent boys or girls (paedophiles) and 32 men sexually attracted to men or women (teleiophiles) were exposed to images of child and adult, male and female faces during a functional magnetic resonance imaging (fMRI) session. A cross-validated, automatic pattern classification algorithm of brain responses to facial stimuli yielded four misclassified participants (three false positives), corresponding to a specificity of 91% and a sensitivity of 95%. These results indicate that the functional response to facial stimuli can be reliably used for fMRI-based classification of paedophilia, bypassing the problem of showing child sexual stimuli to paedophiles.

Face detection in color images using skin color, Laplacian of Gaussian, and Euler number

NASA Astrophysics Data System (ADS)

Saligrama Sundara Raman, Shylaja; Kannanedhi Narasimha Sastry, Balasubramanya Murthy; Subramanyam, Natarajan; Senkutuvan, Ramya; Srikanth, Radhika; John, Nikita; Rao, Prateek

2010-02-01

In this a paper, a feature based approach to face detection has been proposed using an ensemble of algorithms. The method uses chrominance values and edge features to classify the image as skin and nonskin regions. The edge detector used for this purpose is Laplacian of Gaussian (LoG) which is found to be appropriate when images having multiple faces with noise in them. Eight connectivity analysis of these regions will segregate them as probable face or nonface. The procedure is made more robust by identifying local features within these skin regions which include number of holes, percentage of skin and the golden ratio. The method proposed has been tested on color face images of various races obtained from different sources and its performance is found to be encouraging as the color segmentation cleans up almost all the complex facial features. The result obtained has a calculated accuracy of 86.5% on a test set of 230 images.

Fast periodic presentation of natural images reveals a robust face-selective electrophysiological response in the human brain.

PubMed

Rossion, Bruno; Torfs, Katrien; Jacques, Corentin; Liu-Shuang, Joan

2015-01-16

We designed a fast periodic visual stimulation approach to identify an objective signature of face categorization incorporating both visual discrimination (from nonface objects) and generalization (across widely variable face exemplars). Scalp electroencephalographic (EEG) data were recorded in 12 human observers viewing natural images of objects at a rapid frequency of 5.88 images/s for 60 s. Natural images of faces were interleaved every five stimuli, i.e., at 1.18 Hz (5.88/5). Face categorization was indexed by a high signal-to-noise ratio response, specifically at an oddball face stimulation frequency of 1.18 Hz and its harmonics. This face-selective periodic EEG response was highly significant for every participant, even for a single 60-s sequence, and was generally localized over the right occipitotemporal cortex. The periodicity constraint and the large selection of stimuli ensured that this selective response to natural face images was free of low-level visual confounds, as confirmed by the absence of any oddball response for phase-scrambled stimuli. Without any subtraction procedure, time-domain analysis revealed a sequence of differential face-selective EEG components between 120 and 400 ms after oddball face image onset, progressing from medial occipital (P1-faces) to occipitotemporal (N1-faces) and anterior temporal (P2-faces) regions. Overall, this fast periodic visual stimulation approach provides a direct signature of natural face categorization and opens an avenue for efficiently measuring categorization responses of complex visual stimuli in the human brain. © 2015 ARVO.

Near infrared and visible face recognition based on decision fusion of LBP and DCT features

NASA Astrophysics Data System (ADS)

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

2018-03-01

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 order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.

Comparison of different methods for gender estimation from face image of various poses

NASA Astrophysics Data System (ADS)

Ishii, Yohei; Hongo, Hitoshi; Niwa, Yoshinori; Yamamoto, Kazuhiko

2003-04-01

Recently, gender estimation from face images has been studied for frontal facial images. However, it is difficult to obtain such facial images constantly in the case of application systems for security, surveillance and marketing research. In order to build such systems, a method is required to estimate gender from the image of various facial poses. In this paper, three different classifiers are compared in appearance-based gender estimation, which use four directional features (FDF). The classifiers are linear discriminant analysis (LDA), Support Vector Machines (SVMs) and Sparse Network of Winnows (SNoW). Face images used for experiments were obtained from 35 viewpoints. The direction of viewpoints varied +/-45 degrees horizontally, +/-30 degrees vertically at 15 degree intervals respectively. Although LDA showed the best performance for frontal facial images, SVM with Gaussian kernel was found the best performance (86.0%) for the facial images of 35 viewpoints. It is considered that SVM with Gaussian kernel is robust to changes in viewpoint when estimating gender from these results. Furthermore, the estimation rate was quite close to the average estimation rate at 35 viewpoints respectively. It is supposed that the methods are reasonable to estimate gender within the range of experimented viewpoints by learning face images from multiple directions by one class.

Matching Voice and Face Identity from Static Images

ERIC Educational Resources Information Center

Mavica, Lauren W.; Barenholtz, Elan

2013-01-01

Previous research has suggested that people are unable to correctly choose which unfamiliar voice and static image of a face belong to the same person. Here, we present evidence that people can perform this task with greater than chance accuracy. In Experiment 1, participants saw photographs of two, same-gender models, while simultaneously…

A general framework for face reconstruction using single still image based on 2D-to-3D transformation kernel.

PubMed

Fooprateepsiri, Rerkchai; Kurutach, Werasak

2014-03-01

Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Face verification system for Android mobile devices using histogram based features

NASA Astrophysics Data System (ADS)

Sato, Sho; Kobayashi, Kazuhiro; Chen, Qiu

2016-07-01

This paper proposes a face verification system that runs on Android mobile devices. In this system, facial image is captured by a built-in camera on the Android device firstly, and then face detection is implemented using Haar-like features and AdaBoost learning algorithm. The proposed system verify the detected face using histogram based features, which are generated by binary Vector Quantization (VQ) histogram using DCT coefficients in low frequency domains, as well as Improved Local Binary Pattern (Improved LBP) histogram in spatial domain. Verification results with different type of histogram based features are first obtained separately and then combined by weighted averaging. We evaluate our proposed algorithm by using publicly available ORL database and facial images captured by an Android tablet.

Face-n-Food: Gender Differences in Tuning to Faces.

PubMed

Pavlova, Marina A; Scheffler, Klaus; Sokolov, Alexander N

2015-01-01

Faces represent valuable signals for social cognition and non-verbal communication. A wealth of research indicates that women tend to excel in recognition of facial expressions. However, it remains unclear whether females are better tuned to faces. We presented healthy adult females and males with a set of newly created food-plate images resembling faces (slightly bordering on the Giuseppe Arcimboldo style). In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Females not only more readily recognized the images as a face (they reported resembling a face on images, on which males still did not), but gave on overall more face responses. The findings are discussed in the light of gender differences in deficient face perception. As most neuropsychiatric, neurodevelopmental and psychosomatic disorders characterized by social brain abnormalities are sex specific, the task may serve as a valuable tool for uncovering impairments in visual face processing.

Face-n-Food: Gender Differences in Tuning to Faces

PubMed Central

Pavlova, Marina A.; Scheffler, Klaus; Sokolov, Alexander N.

2015-01-01

Faces represent valuable signals for social cognition and non-verbal communication. A wealth of research indicates that women tend to excel in recognition of facial expressions. However, it remains unclear whether females are better tuned to faces. We presented healthy adult females and males with a set of newly created food-plate images resembling faces (slightly bordering on the Giuseppe Arcimboldo style). In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Females not only more readily recognized the images as a face (they reported resembling a face on images, on which males still did not), but gave on overall more face responses. The findings are discussed in the light of gender differences in deficient face perception. As most neuropsychiatric, neurodevelopmental and psychosomatic disorders characterized by social brain abnormalities are sex specific, the task may serve as a valuable tool for uncovering impairments in visual face processing. PMID:26154177

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

PubMed

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-07-23

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

PubMed Central

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-01-01

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other. PMID:26213932

Do Infants Recognize the Arcimboldo Images as Faces? Behavioral and Near-Infrared Spectroscopic Study

ERIC Educational Resources Information Center

Kobayashi, Megumi; Otsuka, Yumiko; Nakato, Emi; Kanazawa, So; Yamaguchi, Masami K.; Kakigi, Ryusuke

2012-01-01

Arcimboldo images induce the perception of faces when shown upright despite the fact that only nonfacial objects such as vegetables and fruits are painted. In the current study, we examined whether infants recognize a face in the Arcimboldo images by using the preferential looking technique and near-infrared spectroscopy (NIRS). In the first…

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

Attention Priority Map of Face Images in Human Early Visual Cortex.

PubMed

Mo, Ce; He, Dongjun; Fang, Fang

2018-01-03

Attention priority maps are topographic representations that are used for attention selection and guidance of task-related behavior during visual processing. Previous studies have identified attention priority maps of simple artificial stimuli in multiple cortical and subcortical areas, but investigating neural correlates of priority maps of natural stimuli is complicated by the complexity of their spatial structure and the difficulty of behaviorally characterizing their priority map. To overcome these challenges, we reconstructed the topographic representations of upright/inverted face images from fMRI BOLD signals in human early visual areas primary visual cortex (V1) and the extrastriate cortex (V2 and V3) based on a voxelwise population receptive field model. We characterized the priority map behaviorally as the first saccadic eye movement pattern when subjects performed a face-matching task relative to the condition in which subjects performed a phase-scrambled face-matching task. We found that the differential first saccadic eye movement pattern between upright/inverted and scrambled faces could be predicted from the reconstructed topographic representations in V1-V3 in humans of either sex. The coupling between the reconstructed representation and the eye movement pattern increased from V1 to V2/3 for the upright faces, whereas no such effect was found for the inverted faces. Moreover, face inversion modulated the coupling in V2/3, but not in V1. Our findings provide new evidence for priority maps of natural stimuli in early visual areas and extend traditional attention priority map theories by revealing another critical factor that affects priority maps in extrastriate cortex in addition to physical salience and task goal relevance: image configuration. SIGNIFICANCE STATEMENT Prominent theories of attention posit that attention sampling of visual information is mediated by a series of interacting topographic representations of visual space known as

Face-selective regions show invariance to linear, but not to non-linear, changes in facial images.

PubMed

Baseler, Heidi A; Young, Andrew W; Jenkins, Rob; Mike Burton, A; Andrews, Timothy J

2016-12-01

Familiar face recognition is remarkably invariant across huge image differences, yet little is understood concerning how image-invariant recognition is achieved. To investigate the neural correlates of invariance, we localized the core face-responsive regions and then compared the pattern of fMR-adaptation to different stimulus transformations in each region to behavioural data demonstrating the impact of the same transformations on familiar face recognition. In Experiment 1, we compared linear transformations of size and aspect ratio to a non-linear transformation affecting only part of the face. We found that adaptation to facial identity in face-selective regions showed invariance to linear changes, but there was no invariance to non-linear changes. In Experiment 2, we measured the sensitivity to non-linear changes that fell within the normal range of variation across face images. We found no adaptation to facial identity for any of the non-linear changes in the image, including to faces that varied in different levels of caricature. These results show a compelling difference in the sensitivity to linear compared to non-linear image changes in face-selective regions of the human brain that is only partially consistent with their effect on behavioural judgements of identity. We conclude that while regions such as the FFA may well be involved in the recognition of face identity, they are more likely to contribute to some form of normalisation that underpins subsequent recognition than to form the neural substrate of recognition per se. Copyright © 2016 Elsevier Ltd. All rights reserved.

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

Standardized food images: A photographing protocol and image database.

PubMed

Charbonnier, Lisette; van Meer, Floor; van der Laan, Laura N; Viergever, Max A; Smeets, Paul A M

2016-01-01

The regulation of food intake has gained much research interest because of the current obesity epidemic. For research purposes, food images are a good and convenient alternative for real food because many dietary decisions are made based on the sight of foods. Food pictures are assumed to elicit anticipatory responses similar to real foods because of learned associations between visual food characteristics and post-ingestive consequences. In contemporary food science, a wide variety of images are used which introduces between-study variability and hampers comparison and meta-analysis of results. Therefore, we created an easy-to-use photographing protocol which enables researchers to generate high resolution food images appropriate for their study objective and population. In addition, we provide a high quality standardized picture set which was characterized in seven European countries. With the use of this photographing protocol a large number of food images were created. Of these images, 80 were selected based on their recognizability in Scotland, Greece and The Netherlands. We collected image characteristics such as liking, perceived calories and/or perceived healthiness ratings from 449 adults and 191 children. The majority of the foods were recognized and liked at all sites. The differences in liking ratings, perceived calories and perceived healthiness between sites were minimal. Furthermore, perceived caloric content and healthiness ratings correlated strongly (r ≥ 0.8) with actual caloric content in both adults and children. The photographing protocol as well as the images and the data are freely available for research use on http://nutritionalneuroscience.eu/. By providing the research community with standardized images and the tools to create their own, comparability between studies will be improved and a head-start is made for a world-wide standardized food image database. Copyright © 2015 Elsevier Ltd. All rights reserved.

Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification.

PubMed

Li, Haoxiang; Hua, Gang

2018-04-01

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic part model. We extract local descriptors (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each descriptor with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of the face parts of all face images in the training corpus, namely the probabilistic elastic part (PEP) model. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms, which naturally defines a part. Given one or multiple face images of the same subject, the PEP-model builds its PEP representation by sequentially concatenating descriptors identified by each Gaussian component in a maximum likelihood sense. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that we achieve state-of-the-art face verification accuracy with the proposed representations on the Labeled Face in the Wild (LFW) dataset, the YouTube video face database, and the CMU MultiPIE dataset.

Optimal Embedding for Shape Indexing in Medical Image Databases

PubMed Central

Qian, Xiaoning; Tagare, Hemant D.; Fulbright, Robert K.; Long, Rodney; Antani, Sameer

2010-01-01

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative. We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance. The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine x-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity. Experimental results included in the paper evaluate (1) the usefulness of shape-similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding. PMID:20163981

Relative expertise affects N170 during selective attention to superimposed face-character images.

PubMed

Ip, Chengteng; Wang, Hailing; Fu, Shimin

2017-07-01

It remains unclear whether the N170 of ERPs reflects domain-specific or domain-general visual object processing. In this study, we used superimposed images of a face and a Chinese character such that participants' relative expertise for the two object types was either similar (Experiment 1 and 2) or different (Experiment 3). Experiment 1 showed that N170 amplitude was larger when participants attended to the character instead of the face of a face-character combination. This result was unchanged in Experiment 2, in which task difficulty was selectively increased for the face component of the combined stimuli. Experiment 3 showed that, although this N170 enhancement for attending to characters relative to faces persisted for false characters with recognizable parts, it disappeared for unrecognizable characters. Therefore, N170 amplitude was significantly greater for Chinese characters than for faces presented within a combined image, independent of the relative task difficulty. This result strongly calls N170 face selectivity into question, demonstrating that, contrary to the expectations established by a domain-specific account, N170 is modulated by expertise. © 2017 Society for Psychophysiological Research.

On the Privacy Protection of Biometric Traits: Palmprint, Face, and Signature

NASA Astrophysics Data System (ADS)

Panigrahy, Saroj Kumar; Jena, Debasish; Korra, Sathya Babu; Jena, Sanjay Kumar

Biometrics are expected to add a new level of security to applications, as a person attempting access must prove who he or she really is by presenting a biometric to the system. The recent developments in the biometrics area have lead to smaller, faster and cheaper systems, which in turn has increased the number of possible application areas for biometric identity verification. The biometric data, being derived from human bodies (and especially when used to identify or verify those bodies) is considered personally identifiable information (PII). The collection, use and disclosure of biometric data — image or template, invokes rights on the part of an individual and obligations on the part of an organization. As biometric uses and databases grow, so do concerns that the personal data collected will not be used in reasonable and accountable ways. Privacy concerns arise when biometric data are used for secondary purposes, invoking function creep, data matching, aggregation, surveillance and profiling. Biometric data transmitted across networks and stored in various databases by others can also be stolen, copied, or otherwise misused in ways that can materially affect the individual involved. As Biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analysed before they are massively deployed in security systems. Along with security, also the privacy of the users is an important factor as the constructions of lines in palmprints contain personal characteristics, from face images a person can be recognised, and fake signatures can be practised by carefully watching the signature images available in the database. We propose a cryptographic approach to encrypt the images of palmprints, faces, and signatures by an advanced Hill cipher technique for hiding the information in the images. It also provides security to these images from being attacked by above mentioned attacks. So, during the feature extraction, the

A novel thermal face recognition approach using face pattern words

NASA Astrophysics Data System (ADS)

Zheng, Yufeng

2010-04-01

A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Matching voice and face identity from static images.

PubMed

Mavica, Lauren W; Barenholtz, Elan

2013-04-01

Previous research has suggested that people are unable to correctly choose which unfamiliar voice and static image of a face belong to the same person. Here, we present evidence that people can perform this task with greater than chance accuracy. In Experiment 1, participants saw photographs of two, same-gender models, while simultaneously listening to a voice recording of one of the models pictured in the photographs and chose which of the two faces they thought belonged to the same model as the recorded voice. We included three conditions: (a) the visual stimuli were frontal headshots (including the neck and shoulders) and the auditory stimuli were recordings of spoken sentences; (b) the visual stimuli only contained cropped faces and the auditory stimuli were full sentences; (c) we used the same pictures as Condition 1 but the auditory stimuli were recordings of a single word. In Experiment 2, participants performed the same task as in Condition 1 of Experiment 1 but with the stimuli presented in sequence. Participants also rated the model's faces and voices along multiple "physical" dimensions (e.g., weight,) or "personality" dimensions (e.g., extroversion); the degree of agreement between the ratings for each model's face and voice was compared to performance for that model in the matching task. In all three conditions, we found that participants chose, at better than chance levels, which faces and voices belonged to the same person. Performance in the matching task was not correlated with the degree of agreement on any of the rated dimensions.

Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics

NASA Astrophysics Data System (ADS)

Chen, Cunjian; Ross, Arun

2013-05-01

Researchers in face recognition have been using Gabor filters for image representation due to their robustness to complex variations in expression and illumination. Numerous methods have been proposed to model the output of filter responses by employing either local or global descriptors. In this work, we propose a novel but simple approach for encoding Gradient information on Gabor-transformed images to represent the face, which can be used for identity, gender and ethnicity assessment. Extensive experiments on the standard face benchmark FERET (Visible versus Visible), as well as the heterogeneous face dataset HFB (Near-infrared versus Visible), suggest that the matching performance due to the proposed descriptor is comparable against state-of-the-art descriptor-based approaches in face recognition applications. Furthermore, the same feature set is used in the framework of a Collaborative Representation Classification (CRC) scheme for deducing soft biometric traits such as gender and ethnicity from face images in the AR, Morph and CAS-PEAL databases.

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

Optimal embedding for shape indexing in medical image databases.

PubMed

Qian, Xiaoning; Tagare, Hemant D; Fulbright, Robert K; Long, Rodney; Antani, Sameer

2010-06-01

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative. We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance. The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity. Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding. Copyright (c) 2010 Elsevier B.V. All rights reserved.

3D FaceCam: a fast and accurate 3D facial imaging device for biometrics applications

NASA Astrophysics Data System (ADS)

Geng, Jason; Zhuang, Ping; May, Patrick; Yi, Steven; Tunnell, David

2004-08-01

Human faces are fundamentally three-dimensional (3D) objects, and each face has its unique 3D geometric profile. The 3D geometric features of a human face can be used, together with its 2D texture, for rapid and accurate face recognition purposes. Due to the lack of low-cost and robust 3D sensors and effective 3D facial recognition (FR) algorithms, almost all existing FR systems use 2D face images. Genex has developed 3D solutions that overcome the inherent problems in 2D while also addressing limitations in other 3D alternatives. One important aspect of our solution is a unique 3D camera (the 3D FaceCam) that combines multiple imaging sensors within a single compact device to provide instantaneous, ear-to-ear coverage of a human face. This 3D camera uses three high-resolution CCD sensors and a color encoded pattern projection system. The RGB color information from each pixel is used to compute the range data and generate an accurate 3D surface map. The imaging system uses no moving parts and combines multiple 3D views to provide detailed and complete 3D coverage of the entire face. Images are captured within a fraction of a second and full-frame 3D data is produced within a few seconds. This described method provides much better data coverage and accuracy in feature areas with sharp features or details (such as the nose and eyes). Using this 3D data, we have been able to demonstrate that a 3D approach can significantly improve the performance of facial recognition. We have conducted tests in which we have varied the lighting conditions and angle of image acquisition in the "field." These tests have shown that the matching results are significantly improved when enrolling a 3D image rather than a single 2D image. With its 3D solutions, Genex is working toward unlocking the promise of powerful 3D FR and transferring FR from a lab technology into a real-world biometric solution.

Motion facilitates face perception across changes in viewpoint and expression in older adults.

PubMed

Maguinness, Corrina; Newell, Fiona N

2014-12-01

Faces are inherently dynamic stimuli. However, face perception in younger adults appears to be mediated by the ability to extract structural cues from static images and a benefit of motion is inconsistent. In contrast, static face processing is poorer and more image-dependent in older adults. We therefore compared the role of facial motion in younger and older adults to assess whether motion can enhance perception when static cues are insufficient. In our studies, older and younger adults learned faces presented in motion or in a sequence of static images, containing rigid (viewpoint) or nonrigid (expression) changes. Immediately following learning, participants matched a static test image to the learned face which varied by viewpoint (Experiment 1) or expression (Experiment 2) and was either learned or novel. First, we found an age effect with better face matching performance in younger than in older adults. However, we observed face matching performance improved in the older adult group, across changes in viewpoint and expression, when faces were learned in motion relative to static presentation. There was no benefit for facial (nonrigid) motion when the task involved matching inverted faces (Experiment 3), suggesting that the ability to use dynamic face information for the purpose of recognition reflects motion encoding which is specific to upright faces. Our results suggest that ageing may offer a unique insight into how dynamic cues support face processing, which may not be readily observed in younger adults' performance. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Hepatitis Diagnosis Using Facial Color Image

NASA Astrophysics Data System (ADS)

Liu, Mingjia; Guo, Zhenhua

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

[Establishment of the database of the 3D facial models for the plastic surgery based on network].

PubMed

Liu, Zhe; Zhang, Hai-Lin; Zhang, Zheng-Guo; Qiao, Qun

2008-07-01

To collect the three-dimensional (3D) facial data of 30 facial deformity patients by the 3D scanner and establish a professional database based on Internet. It can be helpful for the clinical intervention. The primitive point data of face topography were collected by the 3D scanner. Then the 3D point cloud was edited by reverse engineering software to reconstruct the 3D model of the face. The database system was divided into three parts, including basic information, disease information and surgery information. The programming language of the web system is Java. The linkages between every table of the database are credibility. The query operation and the data mining are convenient. The users can visit the database via the Internet and use the image analysis system to observe the 3D facial models interactively. In this paper we presented a database and a web system adapt to the plastic surgery of human face. It can be used both in clinic and in basic research.

Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database

PubMed Central

Keator, David B.; Wei, Dingying; Fennema-Notestine, Christine; Pease, Karen R.; Bockholt, Jeremy; Grethe, Jeffrey S.

2010-01-01

Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site. PMID:20567938

Correction of motion artifacts in endoscopic optical coherence tomography and autofluorescence images based on azimuthal en face image registration.

PubMed

Abouei, Elham; Lee, Anthony M D; Pahlevaninezhad, Hamid; Hohert, Geoffrey; Cua, Michelle; Lane, Pierre; Lam, Stephen; MacAulay, Calum

2018-01-01

We present a method for the correction of motion artifacts present in two- and three-dimensional in vivo endoscopic images produced by rotary-pullback catheters. This method can correct for cardiac/breathing-based motion artifacts and catheter-based motion artifacts such as nonuniform rotational distortion (NURD). This method assumes that en face tissue imaging contains slowly varying structures that are roughly parallel to the pullback axis. The method reduces motion artifacts using a dynamic time warping solution through a cost matrix that measures similarities between adjacent frames in en face images. We optimize and demonstrate the suitability of this method using a real and simulated NURD phantom and in vivo endoscopic pulmonary optical coherence tomography and autofluorescence images. Qualitative and quantitative evaluations of the method show an enhancement of the image quality. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Implementation of a high-speed face recognition system that uses an optical parallel correlator.

PubMed

Watanabe, Eriko; Kodate, Kashiko

2005-02-10

We implement a fully automatic fast face recognition system by using a 1000 frame/s optical parallel correlator designed and assembled by us. The operational speed for the 1:N (i.e., matching one image against N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 s, including the preprocessing and postprocessing times. The binary real-only matched filter is devised for the sake of face recognition, and the system is optimized by the false-rejection rate (FRR) and the false-acceptance rate (FAR), according to 300 samples selected by the biometrics guideline. From trial 1:N identification experiments with the optical parallel correlator, we acquired low error rates of 2.6% FRR and 1.3% FAR. Facial images of people wearing thin glasses or heavy makeup that rendered identification difficult were identified with this system.

DR HAGIS-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients.

PubMed

Holm, Sven; Russell, Greg; Nourrit, Vincent; McLoughlin, Niall

2017-01-01

A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% ([Formula: see text]), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% ([Formula: see text]).

Deep features for efficient multi-biometric recognition with face and ear images

NASA Astrophysics Data System (ADS)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Development of educational image databases and e-books for medical physics training.

PubMed

Tabakov, S; Roberts, V C; Jonsson, B-A; Ljungberg, M; Lewis, C A; Wirestam, R; Strand, S-E; Lamm, I-L; Milano, F; Simmons, A; Deane, C; Goss, D; Aitken, V; Noel, A; Giraud, J-Y; Sherriff, S; Smith, P; Clarke, G; Almqvist, M; Jansson, T

2005-09-01

Medical physics education and training requires the use of extensive imaging material and specific explanations. These requirements provide an excellent background for application of e-Learning. The EU projects Consortia EMERALD and EMIT developed five volumes of such materials, now used in 65 countries. EMERALD developed e-Learning materials in three areas of medical physics (X-ray diagnostic radiology, nuclear medicine and radiotherapy). EMIT developed e-Learning materials in two further areas: ultrasound and magnetic resonance imaging. This paper describes the development of these e-Learning materials (consisting of e-books and educational image databases). The e-books include tasks helping studying of various equipment and methods. The text of these PDF e-books is hyperlinked with respective images. The e-books are used through the readers' own Internet browser. Each Image Database (IDB) includes a browser, which displays hundreds of images of equipment, block diagrams and graphs, image quality examples, artefacts, etc. Both the e-books and IDB are engraved on five separate CD-ROMs. Demo of these materials can be taken from www.emerald2.net.

Visualization and manipulating the image of a formal data structure (FDS)-based database

NASA Astrophysics Data System (ADS)

Verdiesen, Franc; de Hoop, Sylvia; Molenaar, Martien

1994-08-01

A vector map is a terrain representation with a vector-structured geometry. Molenaar formulated an object-oriented formal data structure for 3D single valued vector maps. This FDS is implemented in a database (Oracle). In this study we describe a methodology for visualizing a FDS-based database and manipulating the image. A data set retrieved by querying the database is converted into an import file for a drawing application. An objective of this study is that an end-user can alter and add terrain objects in the image. The drawing application creates an export file, that is compared with the import file. Differences between these files result in updating the database which involves checks on consistency. In this study Autocad is used for visualizing and manipulating the image of the data set. A computer program has been written for the data exchange and conversion between Oracle and Autocad. The data structure of the FDS is compared to the data structure of Autocad and the data of the FDS is converted into the structure of Autocad equal to the FDS.

Enhanced Visualization of Subtle Outer Retinal Pathology by En Face Optical Coherence Tomography and Correlation with Multi-Modal Imaging

PubMed Central

Chew, Avenell L.; Lamey, Tina; McLaren, Terri; De Roach, John

2016-01-01

Purpose To present en face optical coherence tomography (OCT) images generated by graph-search theory algorithm-based custom software and examine correlation with other imaging modalities. Methods En face OCT images derived from high density OCT volumetric scans of 3 healthy subjects and 4 patients using a custom algorithm (graph-search theory) and commercial software (Heidelberg Eye Explorer software (Heidelberg Engineering)) were compared and correlated with near infrared reflectance, fundus autofluorescence, adaptive optics flood-illumination ophthalmoscopy (AO-FIO) and microperimetry. Results Commercial software was unable to generate accurate en face OCT images in eyes with retinal pigment epithelium (RPE) pathology due to segmentation error at the level of Bruch’s membrane (BM). Accurate segmentation of the basal RPE and BM was achieved using custom software. The en face OCT images from eyes with isolated interdigitation or ellipsoid zone pathology were of similar quality between custom software and Heidelberg Eye Explorer software in the absence of any other significant outer retinal pathology. En face OCT images demonstrated angioid streaks, lesions of acute macular neuroretinopathy, hydroxychloroquine toxicity and Bietti crystalline deposits that correlated with other imaging modalities. Conclusions Graph-search theory algorithm helps to overcome the limitations of outer retinal segmentation inaccuracies in commercial software. En face OCT images can provide detailed topography of the reflectivity within a specific layer of the retina which correlates with other forms of fundus imaging. Our results highlight the need for standardization of image reflectivity to facilitate quantification of en face OCT images and longitudinal analysis. PMID:27959968

SCEGRAM: An image database for semantic and syntactic inconsistencies in scenes.

PubMed

Öhlschläger, Sabine; Võ, Melissa Le-Hoa

2017-10-01

Our visual environment is not random, but follows compositional rules according to what objects are usually found where. Despite the growing interest in how such semantic and syntactic rules - a scene grammar - enable effective attentional guidance and object perception, no common image database containing highly-controlled object-scene modifications has been publically available. Such a database is essential in minimizing the risk that low-level features drive high-level effects of interest, which is being discussed as possible source of controversial study results. To generate the first database of this kind - SCEGRAM - we took photographs of 62 real-world indoor scenes in six consistency conditions that contain semantic and syntactic (both mild and extreme) violations as well as their combinations. Importantly, always two scenes were paired, so that an object was semantically consistent in one scene (e.g., ketchup in kitchen) and inconsistent in the other (e.g., ketchup in bathroom). Low-level salience did not differ between object-scene conditions and was generally moderate. Additionally, SCEGRAM contains consistency ratings for every object-scene condition, as well as object-absent scenes and object-only images. Finally, a cross-validation using eye-movements replicated previous results of longer dwell times for both semantic and syntactic inconsistencies compared to consistent controls. In sum, the SCEGRAM image database is the first to contain well-controlled semantic and syntactic object-scene inconsistencies that can be used in a broad range of cognitive paradigms (e.g., verbal and pictorial priming, change detection, object identification, etc.) including paradigms addressing developmental aspects of scene grammar. SCEGRAM can be retrieved for research purposes from http://www.scenegrammarlab.com/research/scegram-database/ .

Database Development for Ocean Impacts: Imaging, Outreach, and Rapid Response

DTIC Science & Technology

2012-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Database Development for Ocean Impacts: Imaging, Outreach...Development for Ocean Impacts: Imaging, Outreach, and Rapid Response 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...hoses ( Applied Ocean Physics & Engineering department, WHOI, to evaluate wear and locate in mooring optical cables used in the Right Whale monitoring

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Comparing features sets for content-based image retrieval in a medical-case database

NASA Astrophysics Data System (ADS)

Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

2004-04-01

Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the

The International Outer Planets Watch atmospheres node database of giant-planet images

NASA Astrophysics Data System (ADS)

Hueso, R.; Legarreta, J.; Sánchez-Lavega, A.; Rojas, J. F.; Gómez-Forrellad, J. M.

2011-10-01

The Atmospheres Node of the International Outer Planets Watch (IOPW) is aimed to encourage the observations and study of the atmospheres of the Giant Planets. One of its main activities is to provide an interaction between the professional and amateur astronomical communities maintaining an online and fully searchable database of images of the giant planets obtained from amateur astronomers and available to both professional and amateurs [1]. The IOPW database contains about 13,000 image observations of Jupiter and Saturn obtained in the visible range with a few contributions of Uranus and Neptune. We describe the organization and structure of the database as posted in the Internet and in particular the PVOL software (Planetary Virtual Observatory & Laboratory) designed to manage the site and based in concepts from Virtual Observatory projects.

Automatic image database generation from CAD for 3D object recognition

NASA Astrophysics Data System (ADS)

Sardana, Harish K.; Daemi, Mohammad F.; Ibrahim, Mohammad K.

1993-06-01

The development and evaluation of Multiple-View 3-D object recognition systems is based on a large set of model images. Due to the various advantages of using CAD, it is becoming more and more practical to use existing CAD data in computer vision systems. Current PC- level CAD systems are capable of providing physical image modelling and rendering involving positional variations in cameras, light sources etc. We have formulated a modular scheme for automatic generation of various aspects (views) of the objects in a model based 3-D object recognition system. These views are generated at desired orientations on the unit Gaussian sphere. With a suitable network file sharing system (NFS), the images can directly be stored on a database located on a file server. This paper presents the image modelling solutions using CAD in relation to multiple-view approach. Our modular scheme for data conversion and automatic image database storage for such a system is discussed. We have used this approach in 3-D polyhedron recognition. An overview of the results, advantages and limitations of using CAD data and conclusions using such as scheme are also presented.

Image Format Conversion to DICOM and Lookup Table Conversion to Presentation Value of the Japanese Society of Radiological Technology (JSRT) Standard Digital Image Database.

PubMed

Yanagita, Satoshi; Imahana, Masato; Suwa, Kazuaki; Sugimura, Hitomi; Nishiki, Masayuki

2016-01-01

Japanese Society of Radiological Technology (JSRT) standard digital image database contains many useful cases of chest X-ray images, and has been used in many state-of-the-art researches. However, the pixel values of all the images are simply digitized as relative density values by utilizing a scanned film digitizer. As a result, the pixel values are completely different from the standardized display system input value of digital imaging and communications in medicine (DICOM), called presentation value (P-value), which can maintain a visual consistency when observing images using different display luminance. Therefore, we converted all the images from JSRT standard digital image database to DICOM format followed by the conversion of the pixel values to P-value using an original program developed by ourselves. Consequently, JSRT standard digital image database has been modified so that the visual consistency of images is maintained among different luminance displays.

Comparison of face types in Chinese women using three-dimensional computed tomography.

PubMed

Zhou, Rong-Rong; Zhao, Qi-Ming; Liu, Miao

2015-04-01

This study compared inverted triangle and square faces of 21 young Chinese Han women (18-25 years old) using three-dimensional computed tomography images retrieved from a records database. In this study, 11 patients had inverted triangle faces and 10 had square faces. The anatomic features were examined and compared. There were significant differences in lower face width, lower face height, masseter thickness, middle/lower face width ratio, and lower face width/height ratio between the two facial types (p < 0.01). Lower face width was positively correlated with masseter thickness and negatively correlated with gonial angle. Lower face height was positively correlated with gonial angle and negatively correlated with masseter thickness, and gonial angle was negatively correlated with masseter thickness. In young Chinese Han women, inverted triangle faces and square faces differ significantly in masseter thickness and lower face height. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

BioImaging Database

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

David Nix, Lisa Simirenko

2006-10-25

The Biolmaging Database (BID) is a relational database developed to store the data and meta-data for the 3D gene expression in early Drosophila embryo development on a cellular level. The schema was written to be used with the MySQL DBMS but with minor modifications can be used on any SQL compliant relational DBMS.

An embedded system for face classification in infrared video using sparse representation

NASA Astrophysics Data System (ADS)

Saavedra M., Antonio; Pezoa, Jorge E.; Zarkesh-Ha, Payman; Figueroa, Miguel

2017-09-01

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Imaging system for creating 3D block-face cryo-images of whole mice

NASA Astrophysics Data System (ADS)

Roy, Debashish; Breen, Michael; Salvado, Olivier; Heinzel, Meredith; McKinley, Eliot; Wilson, David

2006-03-01

We developed a cryomicrotome/imaging system that provides high resolution, high sensitivity block-face images of whole mice or excised organs, and applied it to a variety of biological applications. With this cryo-imaging system, we sectioned cryo-preserved tissues at 2-40 μm thickness and acquired high resolution brightfield and fluorescence images with microscopic in-plane resolution (as good as 1.2 μm). Brightfield images of normal and pathological anatomy show exquisite detail, especially in the abdominal cavity. Multi-planar reformatting and 3D renderings allow one to interrogate 3D structures. In this report, we present brightfield images of mouse anatomy, as well as 3D renderings of organs. For BPK mice model of polycystic kidney disease, we compared brightfield cryo-images and kidney volumes to MRI. The color images provided greater contrast and resolution of cysts as compared to in vivo MRI. We note that color cryo-images are closer to what a researcher sees in dissection, making it easier for them to interpret image data. The combination of field of view, depth of field, ultra high resolution and color/fluorescence contrast enables cryo-image volumes to provide details that cannot be found through in vivo imaging or other ex vivo optical imaging approaches. We believe that this novel imaging system will have applications that include identification of mouse phenotypes, characterization of diseases like blood vessel disease, kidney disease, and cancer, assessment of drug and gene therapy delivery and efficacy and validation of other imaging modalities.

Classroom Laboratory Report: Using an Image Database System in Engineering Education.

ERIC Educational Resources Information Center

Alam, Javed; And Others

1991-01-01

Describes an image database system assembled using separate computer components that was developed to overcome text-only computer hardware storage and retrieval limitations for a pavement design class. (JJK)

Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design.

ERIC Educational Resources Information Center

Stephenson, Christie

1999-01-01

Examines the Museum Educational Site Licensing (MESL) Project--a cooperative project between seven cultural heritage repositories and seven universities--as well as other developments of cultural heritage image databases for academic use. Reviews recent literature on image indexing and retrieval, interface design, and tool development, urging a…

Segmentation of human face using gradient-based approach

NASA Astrophysics Data System (ADS)

Baskan, Selin; Bulut, M. Mete; Atalay, Volkan

2001-04-01

This paper describes a method for automatic segmentation of facial features such as eyebrows, eyes, nose, mouth and ears in color images. This work is an initial step for wide range of applications based on feature-based approaches, such as face recognition, lip-reading, gender estimation, facial expression analysis, etc. Human face can be characterized by its skin color and nearly elliptical shape. For this purpose, face detection is performed using color and shape information. Uniform illumination is assumed. No restrictions on glasses, make-up, beard, etc. are imposed. Facial features are extracted using the vertically and horizontally oriented gradient projections. The gradient of a minimum with respect to its neighbor maxima gives the boundaries of a facial feature. Each facial feature has a different horizontal characteristic. These characteristics are derived by extensive experimentation with many face images. Using fuzzy set theory, the similarity between the candidate and the feature characteristic under consideration is calculated. Gradient-based method is accompanied by the anthropometrical information, for robustness. Ear detection is performed using contour-based shape descriptors. This method detects the facial features and circumscribes each facial feature with the smallest rectangle possible. AR database is used for testing. The developed method is also suitable for real-time systems.

Quantitative Analysis of En Face Spectral-Domain Optical Coherence Tomography Imaging in Polypoidal Choroidal Vasculopathy.

PubMed

Simonett, Joseph M; Chan, Errol W; Chou, Jonathan; Skondra, Dimitra; Colon, Daniel; Chee, Caroline K; Lingam, Gopal; Fawzi, Amani A

2017-02-01

Spectral-domain optical coherence tomography (SD-OCT) imaging can be used to visualize polypoidal choroidal vasculopathy (PCV) lesions in the en face plane. Here, the authors describe a novel lesion quantification technique and compare PCV lesion area measurements and morphology before and after anti-vascular endothelial growth factor (VEGF) treatment. Volumetric SD-OCT scans in eyes with PCV before and after induction anti-VEGF therapy were retrospectively analyzed. En face SD-OCT images were generated and a pixel intensity thresholding process was used to quantify total lesion area. Thirteen eyes with PCV were analyzed. En face SD-OCT PCV lesion area quantification showed good intergrader reliability (intraclass correlation coefficient = 0.944). Total PCV lesion area was significantly reduced after anti-VEGF therapy (2.22 mm 2 vs. 2.73 mm 2 ; P = .02). The overall geographic pattern of the branching vascular network was typically preserved. PCV lesion area analysis using en face SD-OCT is a reproducible tool that can quantify treatment related changes. [Ophthalmic Surg Lasers Imaging Retina. 2017;48:126-133.]. Copyright 2017, SLACK Incorporated.

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

NASA Technical Reports Server (NTRS)

Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.

2012-01-01

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

PubMed Central

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2008-01-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt’s macular dystrophy and retinitis pigmentosa. PMID:17429482

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

NASA Astrophysics Data System (ADS)

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2007-05-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt's macular dystrophy and retinitis pigmentosa.

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

A facial expression image database and norm for Asian population: a preliminary report

NASA Astrophysics Data System (ADS)

Chen, Chien-Chung; Cho, Shu-ling; Horszowska, Katarzyna; Chen, Mei-Yen; Wu, Chia-Ching; Chen, Hsueh-Chih; Yeh, Yi-Yu; Cheng, Chao-Min

2009-01-01

We collected 6604 images of 30 models in eight types of facial expression: happiness, anger, sadness, disgust, fear, surprise, contempt and neutral. Among them, 406 most representative images from 12 models were rated by more than 200 human raters for perceived emotion category and intensity. Such large number of emotion categories, models and raters is sufficient for most serious expression recognition research both in psychology and in computer science. All the models and raters are of Asian background. Hence, this database can also be used when the culture background is a concern. In addition, 43 landmarks each of the 291 rated frontal view images were identified and recorded. This information should facilitate feature based research of facial expression. Overall, the diversity in images and richness in information should make our database and norm useful for a wide range of research.

Combined semantic and similarity search in medical image databases

NASA Astrophysics Data System (ADS)

Seifert, Sascha; Thoma, Marisa; Stegmaier, Florian; Hammon, Matthias; Kramer, Martin; Huber, Martin; Kriegel, Hans-Peter; Cavallaro, Alexander; Comaniciu, Dorin

2011-03-01

The current diagnostic process at hospitals is mainly based on reviewing and comparing images coming from multiple time points and modalities in order to monitor disease progression over a period of time. However, for ambiguous cases the radiologist deeply relies on reference literature or second opinion. Although there is a vast amount of acquired images stored in PACS systems which could be reused for decision support, these data sets suffer from weak search capabilities. Thus, we present a search methodology which enables the physician to fulfill intelligent search scenarios on medical image databases combining ontology-based semantic and appearance-based similarity search. It enabled the elimination of 12% of the top ten hits which would arise without taking the semantic context into account.

Faciotopy—A face-feature map with face-like topology in the human occipital face area

PubMed Central

Henriksson, Linda; Mur, Marieke; Kriegeskorte, Nikolaus

2015-01-01

The occipital face area (OFA) and fusiform face area (FFA) are brain regions thought to be specialized for face perception. However, their intrinsic functional organization and status as cortical areas with well-defined boundaries remains unclear. Here we test these regions for “faciotopy”, a particular hypothesis about their intrinsic functional organisation. A faciotopic area would contain a face-feature map on the cortical surface, where cortical patches represent face features and neighbouring patches represent features that are physically neighbouring in a face. The faciotopy hypothesis is motivated by the idea that face regions might develop from a retinotopic protomap and acquire their selectivity for face features through natural visual experience. Faces have a prototypical configuration of features, are usually perceived in a canonical upright orientation, and are frequently fixated in particular locations. To test the faciotopy hypothesis, we presented images of isolated face features at fixation to subjects during functional magnetic resonance imaging. The responses in V1 were best explained by low-level image properties of the stimuli. OFA, and to a lesser degree FFA, showed evidence for faciotopic organization. When a single patch of cortex was estimated for each face feature, the cortical distances between the feature patches reflected the physical distance between the features in a face. Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance. PMID:26235800

Faciotopy-A face-feature map with face-like topology in the human occipital face area.

PubMed

Henriksson, Linda; Mur, Marieke; Kriegeskorte, Nikolaus

2015-11-01

The occipital face area (OFA) and fusiform face area (FFA) are brain regions thought to be specialized for face perception. However, their intrinsic functional organization and status as cortical areas with well-defined boundaries remains unclear. Here we test these regions for "faciotopy", a particular hypothesis about their intrinsic functional organisation. A faciotopic area would contain a face-feature map on the cortical surface, where cortical patches represent face features and neighbouring patches represent features that are physically neighbouring in a face. The faciotopy hypothesis is motivated by the idea that face regions might develop from a retinotopic protomap and acquire their selectivity for face features through natural visual experience. Faces have a prototypical configuration of features, are usually perceived in a canonical upright orientation, and are frequently fixated in particular locations. To test the faciotopy hypothesis, we presented images of isolated face features at fixation to subjects during functional magnetic resonance imaging. The responses in V1 were best explained by low-level image properties of the stimuli. OFA, and to a lesser degree FFA, showed evidence for faciotopic organization. When a single patch of cortex was estimated for each face feature, the cortical distances between the feature patches reflected the physical distance between the features in a face. Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Integrating image quality in 2nu-SVM biometric match score fusion.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2007-10-01

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

Effective spatial database support for acquiring spatial information from remote sensing images

NASA Astrophysics Data System (ADS)

Jin, Peiquan; Wan, Shouhong; Yue, Lihua

2009-12-01

In this paper, a new approach to maintain spatial information acquiring from remote-sensing images is presented, which is based on Object-Relational DBMS. According to this approach, the detected and recognized results of targets are stored and able to be further accessed in an ORDBMS-based spatial database system, and users can access the spatial information using the standard SQL interface. This approach is different from the traditional ArcSDE-based method, because the spatial information management module is totally integrated into the DBMS and becomes one of the core modules in the DBMS. We focus on three issues, namely the general framework for the ORDBMS-based spatial database system, the definitions of the add-in spatial data types and operators, and the process to develop a spatial Datablade on Informix. The results show that the ORDBMS-based spatial database support for image-based target detecting and recognition is easy and practical to be implemented.

Adaptive gamma correction-based expert system for nonuniform illumination face enhancement

NASA Astrophysics Data System (ADS)

Abdelhamid, Iratni; Mustapha, Aouache; Adel, Oulefki

2018-03-01

The image quality of a face recognition system suffers under severe lighting conditions. Thus, this study aims to develop an approach for nonuniform illumination adjustment based on an adaptive gamma correction (AdaptGC) filter that can solve the aforementioned issue. An approach for adaptive gain factor prediction was developed via neural network model-based cross-validation (NN-CV). To achieve this objective, a gamma correction function and its effects on the face image quality with different gain values were examined first. Second, an orientation histogram (OH) algorithm was assessed as a face's feature descriptor. Subsequently, a density histogram module was developed for face label generation. During the NN-CV construction, the model was assessed to recognize the OH descriptor and predict the face label. The performance of the NN-CV model was evaluated by examining the statistical measures of root mean square error and coefficient of efficiency. Third, to evaluate the AdaptGC enhancement approach, an image quality metric was adopted using enhancement by entropy, contrast per pixel, second-derivative-like measure of enhancement, and sharpness, then supported by visual inspection. The experiment results were examined using five face's databases, namely, extended Yale-B, Carnegie Mellon University-Pose, Illumination, and Expression, Mobio, FERET, and Oulu-CASIA-NIR-VIS. The final results prove that AdaptGC filter implementation compared with state-of-the-art methods is the best choice in terms of contrast and nonuniform illumination adjustment. In summary, the benefits attained prove that AdaptGC is driven by a profitable enhancement rate, which provides satisfying features for high rate face recognition systems.

Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

NASA Astrophysics Data System (ADS)

Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

2013-10-01

Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

Identification using face regions: application and assessment in forensic scenarios.

PubMed

Tome, Pedro; Fierrez, Julian; Vera-Rodriguez, Ruben; Ramos, Daniel

2013-12-10

This paper reports an exhaustive analysis of the discriminative power of the different regions of the human face on various forensic scenarios. In practice, when forensic examiners compare two face images, they focus their attention not only on the overall similarity of the two faces. They carry out an exhaustive morphological comparison region by region (e.g., nose, mouth, eyebrows, etc.). In this scenario it is very important to know based on scientific methods to what extent each facial region can help in identifying a person. This knowledge obtained using quantitative and statical methods on given populations can then be used by the examiner to support or tune his observations. In order to generate such scientific knowledge useful for the expert, several methodologies are compared, such as manual and automatic facial landmarks extraction, different facial regions extractors, and various distances between the subject and the acquisition camera. Also, three scenarios of interest for forensics are considered comparing mugshot and Closed-Circuit TeleVision (CCTV) face images using MORPH and SCface databases. One of the findings is that depending of the acquisition distances, the discriminative power of the facial regions change, having in some cases better performance than the full face. Crown Copyright © 2013. Published by Elsevier Ireland Ltd. All rights reserved.

Adaptive error correction codes for face identification

NASA Astrophysics Data System (ADS)

Hussein, Wafaa R.; Sellahewa, Harin; Jassim, Sabah A.

2012-06-01

Face recognition in uncontrolled environments is greatly affected by fuzziness of face feature vectors as a result of extreme variation in recording conditions (e.g. illumination, poses or expressions) in different sessions. Many techniques have been developed to deal with these variations, resulting in improved performances. This paper aims to model template fuzziness as errors and investigate the use of error detection/correction techniques for face recognition in uncontrolled environments. Error correction codes (ECC) have recently been used for biometric key generation but not on biometric templates. We have investigated error patterns in binary face feature vectors extracted from different image windows of differing sizes and for different recording conditions. By estimating statistical parameters for the intra-class and inter-class distributions of Hamming distances in each window, we encode with appropriate ECC's. The proposed approached is tested for binarised wavelet templates using two face databases: Extended Yale-B and Yale. We shall demonstrate that using different combinations of BCH-based ECC's for different blocks and different recording conditions leads to in different accuracy rates, and that using ECC's results in significantly improved recognition results.

The other-race effect in face learning: Using naturalistic images to investigate face ethnicity effects in a learning paradigm.

PubMed

Hayward, William G; Favelle, Simone K; Oxner, Matt; Chu, Ming Hon; Lam, Sze Man

2017-05-01

The other-race effect in face identification has been reported in many situations and by many different ethnicities, yet it remains poorly understood. One reason for this lack of clarity may be a limitation in the methodologies that have been used to test it. Experiments typically use an old-new recognition task to demonstrate the existence of the other-race effect, but such tasks are susceptible to different social and perceptual influences, particularly in terms of the extent to which all faces are equally individuated at study. In this paper we report an experiment in which we used a face learning methodology to measure the other-race effect. We obtained naturalistic photographs of Chinese and Caucasian individuals, which allowed us to test the ability of participants to generalize their learning to new ecologically valid exemplars of a face identity. We show a strong own-race advantage in face learning, such that participants required many fewer trials to learn names of own-race individuals than those of other-race individuals and were better able to identify learned own-race individuals in novel naturalistic stimuli. Since our methodology requires individuation of all faces, and generalization over large image changes, our finding of an other-race effect can be attributed to a specific deficit in the sensitivity of perceptual and memory processes to other-race faces.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

PubMed

Kostopoulos, Spiros A; Asvestas, Pantelis A; Kalatzis, Ioannis K; Sakellaropoulos, George C; Sakkis, Theofilos H; Cavouras, Dionisis A; Glotsos, Dimitris T

2017-09-01

The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77

A Two-Stage Framework for 3D Face Reconstruction from RGBD Images.

PubMed

Wang, Kangkan; Wang, Xianwang; Pan, Zhigeng; Liu, Kai

2014-08-01

This paper proposes a new approach for 3D face reconstruction with RGBD images from an inexpensive commodity sensor. The challenges we face are: 1) substantial random noise and corruption are present in low-resolution depth maps; and 2) there is high degree of variability in pose and face expression. We develop a novel two-stage algorithm that effectively maps low-quality depth maps to realistic face models. Each stage is targeted toward a certain type of noise. The first stage extracts sparse errors from depth patches through the data-driven local sparse coding, while the second stage smooths noise on the boundaries between patches and reconstructs the global shape by combining local shapes using our template-based surface refinement. Our approach does not require any markers or user interaction. We perform quantitative and qualitative evaluations on both synthetic and real test sets. Experimental results show that the proposed approach is able to produce high-resolution 3D face models with high accuracy, even if inputs are of low quality, and have large variations in viewpoint and face expression.

The FoodCast research image database (FRIDa)

PubMed Central

Foroni, Francesco; Pergola, Giulio; Argiris, Georgette; Rumiati, Raffaella I.

2013-01-01

In recent years we have witnessed an increasing interest in food processing and eating behaviors. This is probably due to several reasons. The biological relevance of food choices, the complexity of the food-rich environment in which we presently live (making food-intake regulation difficult), and the increasing health care cost due to illness associated with food (food hazards, food contamination, and aberrant food-intake). Despite the importance of the issues and the relevance of this research, comprehensive and validated databases of stimuli are rather limited, outdated, or not available for non-commercial purposes to independent researchers who aim at developing their own research program. The FoodCast Research Image Database (FRIDa) we present here includes 877 images belonging to eight different categories: natural-food (e.g., strawberry), transformed-food (e.g., french fries), rotten-food (e.g., moldy banana), natural-non-food items (e.g., pinecone), artificial food-related objects (e.g., teacup), artificial objects (e.g., guitar), animals (e.g., camel), and scenes (e.g., airport). FRIDa has been validated on a sample of healthy participants (N = 73) on standard variables (e.g., valence, familiarity, etc.) as well as on other variables specifically related to food items (e.g., perceived calorie content); it also includes data on the visual features of the stimuli (e.g., brightness, high frequency power, etc.). FRIDa is a well-controlled, flexible, validated, and freely available (http://foodcast.sissa.it/neuroscience/) tool for researchers in a wide range of academic fields and industry. PMID:23459781

Food-pics: an image database for experimental research on eating and appetite.

PubMed

Blechert, Jens; Meule, Adrian; Busch, Niko A; Ohla, Kathrin

2014-01-01

Our current environment is characterized by the omnipresence of food cues. The sight and smell of real foods, but also graphically depictions of appetizing foods, can guide our eating behavior, for example, by eliciting food craving and influencing food choice. The relevance of visual food cues on human information processing has been demonstrated by a growing body of studies employing food images across the disciplines of psychology, medicine, and neuroscience. However, currently used food image sets vary considerably across laboratories and image characteristics (contrast, brightness, etc.) and food composition (calories, macronutrients, etc.) are often unspecified. These factors might have contributed to some of the inconsistencies of this research. To remedy this, we developed food-pics, a picture database comprising 568 food images and 315 non-food images along with detailed meta-data. A total of N = 1988 individuals with large variance in age and weight from German speaking countries and North America provided normative ratings of valence, arousal, palatability, desire to eat, recognizability and visual complexity. Furthermore, data on macronutrients (g), energy density (kcal), and physical image characteristics (color composition, contrast, brightness, size, complexity) are provided. The food-pics image database is freely available under the creative commons license with the hope that the set will facilitate standardization and comparability across studies and advance experimental research on the determinants of eating behavior.

Food-pics: an image database for experimental research on eating and appetite

PubMed Central

Blechert, Jens; Meule, Adrian; Busch, Niko A.; Ohla, Kathrin

2014-01-01

Our current environment is characterized by the omnipresence of food cues. The sight and smell of real foods, but also graphically depictions of appetizing foods, can guide our eating behavior, for example, by eliciting food craving and influencing food choice. The relevance of visual food cues on human information processing has been demonstrated by a growing body of studies employing food images across the disciplines of psychology, medicine, and neuroscience. However, currently used food image sets vary considerably across laboratories and image characteristics (contrast, brightness, etc.) and food composition (calories, macronutrients, etc.) are often unspecified. These factors might have contributed to some of the inconsistencies of this research. To remedy this, we developed food-pics, a picture database comprising 568 food images and 315 non-food images along with detailed meta-data. A total of N = 1988 individuals with large variance in age and weight from German speaking countries and North America provided normative ratings of valence, arousal, palatability, desire to eat, recognizability and visual complexity. Furthermore, data on macronutrients (g), energy density (kcal), and physical image characteristics (color composition, contrast, brightness, size, complexity) are provided. The food-pics image database is freely available under the creative commons license with the hope that the set will facilitate standardization and comparability across studies and advance experimental research on the determinants of eating behavior. PMID:25009514

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.

Adaptive non-local smoothing-based weberface for illumination-insensitive face recognition

NASA Astrophysics Data System (ADS)

Yao, Min; Zhu, Changming

2017-07-01

Compensating the illumination of a face image is an important process to achieve effective face recognition under severe illumination conditions. This paper present a novel illumination normalization method which specifically considers removing the illumination boundaries as well as reducing the regional illumination. We begin with the analysis of the commonly used reflectance model and then expatiate the hybrid usage of adaptive non-local smoothing and the local information coding based on Weber's law. The effectiveness and advantages of this combination are evidenced visually and experimentally. Results on Extended YaleB database show its better performance than several other famous methods.

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

PubMed

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-28

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

PubMed Central

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-01

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. PMID:26828496

Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification.

PubMed

Yang, Fan; Paindavoine, M

2003-01-01

This paper describes a real time vision system that allows us to localize faces in video sequences and verify their identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and identity verification are 92% (FPGA), 85% (ZISC), and 98.2% (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.

Face recognition based on two-dimensional discriminant sparse preserving projection

NASA Astrophysics Data System (ADS)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

Facial soft biometric features for forensic face recognition.

PubMed

Tome, Pedro; Vera-Rodriguez, Ruben; Fierrez, Julian; Ortega-Garcia, Javier

2015-12-01

This paper proposes a functional feature-based approach useful for real forensic caseworks, based on the shape, orientation and size of facial traits, which can be considered as a soft biometric approach. The motivation of this work is to provide a set of facial features, which can be understood by non-experts such as judges and support the work of forensic examiners who, in practice, carry out a thorough manual comparison of face images paying special attention to the similarities and differences in shape and size of various facial traits. This new approach constitutes a tool that automatically converts a set of facial landmarks to a set of features (shape and size) corresponding to facial regions of forensic value. These features are furthermore evaluated in a population to generate statistics to support forensic examiners. The proposed features can also be used as additional information that can improve the performance of traditional face recognition systems. These features follow the forensic methodology and are obtained in a continuous and discrete manner from raw images. A statistical analysis is also carried out to study the stability, discrimination power and correlation of the proposed facial features on two realistic databases: MORPH and ATVS Forensic DB. Finally, the performance of both continuous and discrete features is analyzed using different similarity measures. Experimental results show high discrimination power and good recognition performance, especially for continuous features. A final fusion of the best systems configurations achieves rank 10 match results of 100% for ATVS database and 75% for MORPH database demonstrating the benefits of using this information in practice. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Self-compassion in the face of shame and body image dissatisfaction: implications for eating disorders.

PubMed

Ferreira, Cláudia; Pinto-Gouveia, José; Duarte, Cristiana

2013-04-01

The current study examines the role of self-compassion in face of shame and body image dissatisfaction, in 102 female eating disorders' patients, and 123 women from general population. Self-compassion was negatively associated with external shame, general psychopathology, and eating disorders' symptomatology. In women from the general population increased external shame predicted drive for thinness partially through lower self-compassion; also, body image dissatisfaction directly predicted drive for thinness. However, in the patients' sample increased shame and body image dissatisfaction predicted increased drive for thinness through decreased self-compassion. These results highlight the importance of the affiliative emotion dimensions of self-compassion in face of external shame, body image dissatisfaction and drive for thinness, emphasising the relevance of cultivating a self-compassionate relationship in eating disorders' patients. Copyright © 2013. Published by Elsevier Ltd.

Social Inferences from Faces: Ambient Images Generate a Three-Dimensional Model

ERIC Educational Resources Information Center

Sutherland, Clare A. M.; Oldmeadow, Julian A.; Santos, Isabel M.; Towler, John; Burt, D. Michael; Young, Andrew W.

2013-01-01

Three experiments are presented that investigate the two-dimensional valence/trustworthiness by dominance model of social inferences from faces (Oosterhof & Todorov, 2008). Experiment 1 used image averaging and morphing techniques to demonstrate that consistent facial cues subserve a range of social inferences, even in a highly variable sample of…

Acute Solar Retinopathy Imaged With Adaptive Optics, Optical Coherence Tomography Angiography, and En Face Optical Coherence Tomography.

PubMed

Wu, Chris Y; Jansen, Michael E; Andrade, Jorge; Chui, Toco Y P; Do, Anna T; Rosen, Richard B; Deobhakta, Avnish

2018-01-01

Solar retinopathy is a rare form of retinal injury that occurs after direct sungazing. To enhance understanding of the structural changes that occur in solar retinopathy by obtaining high-resolution in vivo en face images. Case report of a young adult woman who presented to the New York Eye and Ear Infirmary with symptoms of acute solar retinopathy after viewing the solar eclipse on August 21, 2017. Results of comprehensive ophthalmic examination and images obtained by fundus photography, microperimetry, spectral-domain optical coherence tomography (OCT), adaptive optics scanning light ophthalmoscopy, OCT angiography, and en face OCT. The patient was examined after viewing the solar eclipse. Visual acuity was 20/20 OD and 20/25 OS. The patient was left-eye dominant. Spectral-domain OCT images were consistent with mild and severe acute solar retinopathy in the right and left eye, respectively. Microperimetry was normal in the right eye but showed paracentral decreased retinal sensitivity in the left eye with a central absolute scotoma. Adaptive optics images of the right eye showed a small region of nonwaveguiding photoreceptors, while images of the left eye showed a large area of abnormal and nonwaveguiding photoreceptors. Optical coherence tomography angiography images were normal in both eyes. En face OCT images of the right eye showed a small circular hyperreflective area, with central hyporeflectivity in the outer retina of the right eye. The left eye showed a hyperreflective lesion that intensified in area from inner to middle retina and became mostly hyporeflective in the outer retina. The shape of the lesion on adaptive optics and en face OCT images of the left eye corresponded to the shape of the scotoma drawn by the patient on Amsler grid. Acute solar retinopathy can present with foveal cone photoreceptor mosaic disturbances on adaptive optics scanning light ophthalmoscopy imaging. Corresponding reflectivity changes can be seen on en face OCT, especially

Sorted Index Numbers for Privacy Preserving Face Recognition

NASA Astrophysics Data System (ADS)

Wang, Yongjin; Hatzinakos, Dimitrios

2009-12-01

This paper presents a novel approach for changeable and privacy preserving face recognition. We first introduce a new method of biometric matching using the sorted index numbers (SINs) of feature vectors. Since it is impossible to recover any of the exact values of the original features, the transformation from original features to the SIN vectors is noninvertible. To address the irrevocable nature of biometric signals whilst obtaining stronger privacy protection, a random projection-based method is employed in conjunction with the SIN approach to generate changeable and privacy preserving biometric templates. The effectiveness of the proposed method is demonstrated on a large generic data set, which contains images from several well-known face databases. Extensive experimentation shows that the proposed solution may improve the recognition accuracy.

Face recognition system and method using face pattern words and face pattern bytes

DOEpatents

Zheng, Yufeng

2014-12-23

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power

A framework for the recognition of 3D faces and expressions

NASA Astrophysics Data System (ADS)

Li, Chao; Barreto, Armando

2006-04-01

Face recognition technology has been a focus both in academia and industry for the last couple of years because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent with 2D face recognition, i.e. sensitivity to illumination conditions and orientation positioning of the subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: an expression recognition system, a system for the identification of faces with expression, and neutral face recognition system. A system for the recognition of faces with one type of expression (happiness) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

Vitiligo on the face (image)

MedlinePlus

This is a picture of vitiligo on the face. Complete loss of melanin, the primary skin pigment, ... the same areas on both sides of the face -- symmetrically -- or it may be patchy -- asymmetrical. The ...

Statistical organelle dissection of Arabidopsis guard cells using image database LIPS.

PubMed

Higaki, Takumi; Kutsuna, Natsumaro; Hosokawa, Yoichiroh; Akita, Kae; Ebine, Kazuo; Ueda, Takashi; Kondo, Noriaki; Hasezawa, Seiichiro

2012-01-01

To comprehensively grasp cell biological events in plant stomatal movement, we have captured microscopic images of guard cells with various organelles markers. The 28,530 serial optical sections of 930 pairs of Arabidopsis guard cells have been released as a new image database, named Live Images of Plant Stomata (LIPS). We visualized the average organellar distributions in guard cells using probabilistic mapping and image clustering techniques. The results indicated that actin microfilaments and endoplasmic reticulum (ER) are mainly localized to the dorsal side and connection regions of guard cells. Subtractive images of open and closed stomata showed distribution changes in intracellular structures, including the ER, during stomatal movement. Time-lapse imaging showed that similar ER distribution changes occurred during stomatal opening induced by light irradiation or femtosecond laser shots on neighboring epidermal cells, indicating that our image analysis approach has identified a novel ER relocation in stomatal opening.

The UBIRIS.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance.

PubMed

Proença, Hugo; Filipe, Sílvio; Santos, Ricardo; Oliveira, João; Alexandre, Luís A

2010-08-01

The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.

Serial block face scanning electron microscopy--the future of cell ultrastructure imaging.

PubMed

Hughes, Louise; Hawes, Chris; Monteith, Sandy; Vaughan, Sue

2014-03-01

One of the major drawbacks in transmission electron microscopy has been the production of three-dimensional views of cells and tissues. Currently, there is no one suitable 3D microscopy technique that answers all questions and serial block face scanning electron microscopy (SEM) fills the gap between 3D imaging using high-end fluorescence microscopy and the high resolution offered by electron tomography. In this review, we discuss the potential of the serial block face SEM technique for studying the three-dimensional organisation of animal, plant and microbial cells.

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

PubMed

Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

2017-01-01

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation.

PubMed

Yang, Hongyu; Huang, Di; Wang, Yunhong; Wang, Heng; Tang, Yuanyan

2016-06-01

Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Standoff imaging of a masked human face using a 670 GHz high resolution radar

NASA Astrophysics Data System (ADS)

Kjellgren, Jan; Svedin, Jan; Cooper, Ken B.

2011-11-01

This paper presents an exploratory attempt to use high-resolution radar measurements for face identification in forensic applications. An imaging radar system developed by JPL was used to measure a human face at 670 GHz. Frontal views of the face were measured both with and without a ski mask at a range of 25 m. The realized spatial resolution was roughly 1 cm in all three dimensions. The surfaces of the ski mask and the face were detected by using the two dominating reflections from amplitude data. Various methods for visualization of these surfaces are presented. The possibility to use radar data to determine certain face distance measures between well-defined face landmarks, typically used for anthropometric statistics, was explored. The measures used here were face length, frontal breadth and interpupillary distance. In many cases the radar system seems to provide sufficient information to exclude an innocent subject from suspicion. For an accurate identification it is believed that a system must provide significantly more information.

Rear-facing car seat (image)

MedlinePlus

A rear-facing car seat position is recommended for a child who is very young. Extreme injury can occur in an accident because ... child. In a frontal crash a rear-facing car seat is best, because it cradles the head, ...

Stable face representations

PubMed Central

Jenkins, Rob; Burton, A. Mike

2011-01-01

Photographs are often used to establish the identity of an individual or to verify that they are who they claim to be. Yet, recent research shows that it is surprisingly difficult to match a photo to a face. Neither humans nor machines can perform this task reliably. Although human perceivers are good at matching familiar faces, performance with unfamiliar faces is strikingly poor. The situation is no better for automatic face recognition systems. In practical settings, automatic systems have been consistently disappointing. In this review, we suggest that failure to distinguish between familiar and unfamiliar face processing has led to unrealistic expectations about face identification in applied settings. We also argue that a photograph is not necessarily a reliable indicator of facial appearance, and develop our proposal that summary statistics can provide more stable face representations. In particular, we show that image averaging stabilizes facial appearance by diluting aspects of the image that vary between snapshots of the same person. We review evidence that the resulting images can outperform photographs in both behavioural experiments and computer simulations, and outline promising directions for future research. PMID:21536553

Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma

PubMed Central

Carey, George B; Kazantsev, Stephanie; Surati, Mosmi; Rolle, Cleo E; Kanteti, Archana; Sadiq, Ahad; Bahroos, Neil; Raumann, Brigitte; Madduri, Ravi; Dave, Paul; Starkey, Adam; Hensing, Thomas; Husain, Aliya N; Vokes, Everett E; Vigneswaran, Wickii; Armato, Samuel G; Kindler, Hedy L; Salgia, Ravi

2012-01-01

Objective An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation. Background We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database. Methods The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A. Results A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data. Discussion We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research. Conclusions The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger

EROS Main Image File: A Picture Perfect Database for Landsat Imagery and Aerial Photography.

ERIC Educational Resources Information Center

Jack, Robert F.

1984-01-01

Describes Earth Resources Observation System online database, which provides access to computerized images of Earth obtained via satellite. Highlights include retrieval system and commands, types of images, search strategies, other online functions, and interpretation of accessions. Satellite information, sources and samples of accessions, and…

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

NASA Astrophysics Data System (ADS)

Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

2018-04-01

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, X.

2013-07-01

The China Near Earth Object Survey Telescope (CNEOST) is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgradation of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, Xin

2014-04-01

The China Near Earth Object Survey Telescope is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgrade of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of the massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.

PubMed

Ferreira Junior, José Raniery; Oliveira, Marcelo Costa; de Azevedo-Marques, Paulo Mazzoncini

2016-12-01

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

Large image microscope array for the compilation of multimodality whole organ image databases.

PubMed

Namati, Eman; De Ryk, Jessica; Thiesse, Jacqueline; Towfic, Zaid; Hoffman, Eric; Mclennan, Geoffrey

2007-11-01

Three-dimensional, structural and functional digital image databases have many applications in education, research, and clinical medicine. However, to date, apart from cryosectioning, there have been no reliable means to obtain whole-organ, spatially conserving histology. Our aim was to generate a system capable of acquiring high-resolution images, featuring microscopic detail that could still be spatially correlated to the whole organ. To fulfill these objectives required the construction of a system physically capable of creating very fine whole-organ sections and collecting high-magnification and resolution digital images. We therefore designed a large image microscope array (LIMA) to serially section and image entire unembedded organs while maintaining the structural integrity of the tissue. The LIMA consists of several integrated components: a novel large-blade vibrating microtome, a 1.3 megapixel peltier cooled charge-coupled device camera, a high-magnification microscope, and a three axis gantry above the microtome. A custom control program was developed to automate the entire sectioning and automated raster-scan imaging sequence. The system is capable of sectioning unembedded soft tissue down to a thickness of 40 microm at specimen dimensions of 200 x 300 mm to a total depth of 350 mm. The LIMA system has been tested on fixed lung from sheep and mice, resulting in large high-quality image data sets, with minimal distinguishable disturbance in the delicate alveolar structures. Copyright 2007 Wiley-Liss, Inc.

Pathology Imagebase-a reference image database for standardization of pathology.

PubMed

Egevad, Lars; Cheville, John; Evans, Andrew J; Hörnblad, Jonas; Kench, James G; Kristiansen, Glen; Leite, Katia R M; Magi-Galluzzi, Cristina; Pan, Chin-Chen; Samaratunga, Hemamali; Srigley, John R; True, Lawrence; Zhou, Ming; Clements, Mark; Delahunt, Brett

2017-11-01

Despite efforts to standardize histopathology practice through the development of guidelines, the interpretation of morphology is still hampered by subjectivity. We here describe Pathology Imagebase, a novel mechanism for establishing an international standard for the interpretation of pathology specimens. The International Society of Urological Pathology (ISUP) established a reference image database through the input of experts in the field. Three panels were formed, one each for prostate, urinary bladder and renal pathology, consisting of 24 international experts. Each of the panel members uploaded microphotographs of cases into a non-public database. The remaining 23 experts were asked to vote from a multiple-choice menu. Prior to and while voting, panel members were unable to access the results of voting by the other experts. When a consensus level of at least two-thirds or 16 votes was reached, cases were automatically transferred to the main database. Consensus was reached in a total of 287 cases across five projects on the grading of prostate, bladder and renal cancer and the classification of renal tumours and flat lesions of the bladder. The full database is available to all ISUP members at www.isupweb.org. Non-members may access a selected number of cases. It is anticipated that the database will assist pathologists in calibrating their grading, and will also promote consistency in the diagnosis of difficult cases. © 2017 John Wiley & Sons Ltd.

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Individual differences in face-looking behavior generalize from the lab to the world.

PubMed

Peterson, Matthew F; Lin, Jing; Zaun, Ian; Kanwisher, Nancy

2016-05-01

Recent laboratory studies have found large, stable individual differences in the location people first fixate when identifying faces, ranging from the brows to the mouth. Importantly, this variation is strongly associated with differences in fixation-specific identification performance such that individuals' recognition ability is maximized when looking at their preferred location (Mehoudar, Arizpe, Baker, & Yovel, 2014; Peterson & Eckstein, 2013). This finding suggests that face representations are retinotopic and individuals enact gaze strategies that optimize identification, yet the extent to which this behavior reflects real-world gaze behavior is unknown. Here, we used mobile eye trackers to test whether individual differences in face gaze generalize from lab to real-world vision. In-lab fixations were measured with a speeded face identification task, while real-world behavior was measured as subjects freely walked around the Massachusetts Institute of Technology campus. We found a strong correlation between the patterns of individual differences in face gaze in the lab and real-world settings. Our findings support the hypothesis that individuals optimize real-world face identification by consistently fixating the same location and thus strongly constraining the space of retinotopic input. The methods developed for this study entailed collecting a large set of high-definition, wide field-of-view natural videos from head-mounted cameras and the viewer's fixation position, allowing us to characterize subjects' actually experienced real-world retinotopic images. These images enable us to ask how vision is optimized not just for the statistics of the "natural images" found in web databases, but of the truly natural, retinotopic images that have landed on actual human retinae during real-world experience.

Hierarchical ensemble of global and local classifiers for face recognition.

PubMed

Su, Yu; Shan, Shiguang; Chen, Xilin; Gao, Wen

2009-08-01

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

Smiles in face matching: Idiosyncratic information revealed through a smile improves unfamiliar face matching performance.

PubMed

Mileva, Mila; Burton, A Mike

2018-06-19

Unfamiliar face matching is a surprisingly difficult task, yet we often rely on people's matching decisions in applied settings (e.g., border control). Most attempts to improve accuracy (including training and image manipulation) have had very limited success. In a series of studies, we demonstrate that using smiling rather than neutral pairs of images brings about significant improvements in face matching accuracy. This is true for both match and mismatch trials, implying that the information provided through a smile helps us detect images of the same identity as well as distinguishing between images of different identities. Study 1 compares matching performance when images in the face pair display either an open-mouth smile or a neutral expression. In Study 2, we add an intermediate level, closed-mouth smile, to identify the effect of teeth being exposed, and Study 3 explores face matching accuracy when only information about the lower part of the face is available. Results demonstrate that an open-mouth smile changes the face in an idiosyncratic way which aids face matching decisions. Such findings have practical implications for matching in the applied context where we typically use neutral images to represent ourselves in official documents. © 2018 The British Psychological Society.

Social Cognition in Williams Syndrome: Face Tuning

PubMed Central

Pavlova, Marina A.; Heiz, Julie; Sokolov, Alexander N.; Barisnikov, Koviljka

2016-01-01

Many neurological, neurodevelopmental, neuropsychiatric, and psychosomatic disorders are characterized by impairments in visual social cognition, body language reading, and facial assessment of a social counterpart. Yet a wealth of research indicates that individuals with Williams syndrome exhibit remarkable concern for social stimuli and face fascination. Here individuals with Williams syndrome were presented with a set of Face-n-Food images composed of food ingredients and in different degree resembling a face (slightly bordering on the Giuseppe Arcimboldo style). The primary advantage of these images is that single components do not explicitly trigger face-specific processing, whereas in face images commonly used for investigating face perception (such as photographs or depictions), the mere occurrence of typical cues already implicates face presence. In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Strikingly, individuals with Williams syndrome exhibited profound deficits in recognition of the Face-n-Food images as a face: they did not report seeing a face on the images, which typically developing controls effortlessly recognized as a face, and gave overall fewer face responses. This suggests atypical face tuning in Williams syndrome. The outcome is discussed in the light of a general pattern of social cognition in Williams syndrome and brain mechanisms underpinning face processing. PMID:27531986

Social Cognition in Williams Syndrome: Face Tuning.

PubMed

Pavlova, Marina A; Heiz, Julie; Sokolov, Alexander N; Barisnikov, Koviljka

2016-01-01

Many neurological, neurodevelopmental, neuropsychiatric, and psychosomatic disorders are characterized by impairments in visual social cognition, body language reading, and facial assessment of a social counterpart. Yet a wealth of research indicates that individuals with Williams syndrome exhibit remarkable concern for social stimuli and face fascination. Here individuals with Williams syndrome were presented with a set of Face-n-Food images composed of food ingredients and in different degree resembling a face (slightly bordering on the Giuseppe Arcimboldo style). The primary advantage of these images is that single components do not explicitly trigger face-specific processing, whereas in face images commonly used for investigating face perception (such as photographs or depictions), the mere occurrence of typical cues already implicates face presence. In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Strikingly, individuals with Williams syndrome exhibited profound deficits in recognition of the Face-n-Food images as a face: they did not report seeing a face on the images, which typically developing controls effortlessly recognized as a face, and gave overall fewer face responses. This suggests atypical face tuning in Williams syndrome. The outcome is discussed in the light of a general pattern of social cognition in Williams syndrome and brain mechanisms underpinning face processing.

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

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

PubMed

Cho, Woon; Jang, Jinbeum; Koschan, Andreas; Abidi, Mongi A; Paik, Joonki

2016-11-28

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

Face liveness detection using shearlet-based feature descriptors

NASA Astrophysics Data System (ADS)

Feng, Litong; Po, Lai-Man; Li, Yuming; Yuan, Fang

2016-07-01

Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by nonreal faces such as photographs or videos of valid users. The antispoof problem must be well resolved before widely applying face recognition in our daily life. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to propose a feature descriptor and an efficient framework that can be used to effectively deal with the face liveness detection problem. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and a softmax classifier are concatenated to detect face liveness. We evaluated this approach using the CASIA Face antispoofing database and replay-attack database. The experimental results show that our approach performs better than the state-of-the-art techniques following the provided protocols of these databases, and it is possible to significantly enhance the security of the face recognition biometric system. In addition, the experimental results also demonstrate that this framework can be easily extended to classify different spoofing attacks.

The Center for Integrated Molecular Brain Imaging (Cimbi) database.

PubMed

Knudsen, Gitte M; Jensen, Peter S; Erritzoe, David; Baaré, William F C; Ettrup, Anders; Fisher, Patrick M; Gillings, Nic; Hansen, Hanne D; Hansen, Lars Kai; Hasselbalch, Steen G; Henningsson, Susanne; Herth, Matthias M; Holst, Klaus K; Iversen, Pernille; Kessing, Lars V; Macoveanu, Julian; Madsen, Kathrine Skak; Mortensen, Erik L; Nielsen, Finn Årup; Paulson, Olaf B; Siebner, Hartwig R; Stenbæk, Dea S; Svarer, Claus; Jernigan, Terry L; Strother, Stephen C; Frokjaer, Vibe G

2016-01-01

We here describe a multimodality neuroimaging containing data from healthy volunteers and patients, acquired within the Lundbeck Foundation Center for Integrated Molecular Brain Imaging (Cimbi) in Copenhagen, Denmark. The data is of particular relevance for neurobiological research questions related to the serotonergic transmitter system with its normative data on the serotonergic subtype receptors 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 and the 5-HT transporter (5-HTT), but can easily serve other purposes. The Cimbi database and Cimbi biobank were formally established in 2008 with the purpose to store the wealth of Cimbi-acquired data in a highly structured and standardized manner in accordance with the regulations issued by the Danish Data Protection Agency as well as to provide a quality-controlled resource for future hypothesis-generating and hypothesis-driven studies. The Cimbi database currently comprises a total of 1100 PET and 1000 structural and functional MRI scans and it holds a multitude of additional data, such as genetic and biochemical data, and scores from 17 self-reported questionnaires and from 11 neuropsychological paper/computer tests. The database associated Cimbi biobank currently contains blood and in some instances saliva samples from about 500 healthy volunteers and 300 patients with e.g., major depression, dementia, substance abuse, obesity, and impulsive aggression. Data continue to be added to the Cimbi database and biobank. Copyright © 2015. Published by Elsevier Inc.

Using functional magnetic resonance imaging to explore the flashed face distortion effect.

PubMed

Wen, Tanya; Kung, Chun-Chia

2014-10-27

The flashed face distortion (FFD) effect was coined by Tangen, Murphy, and Thompson (2011) in their second-place winner of the 2012 Best Illusion of the Year Contest. The FFD arises when people view various eye-aligned faces that are sequentially flashed in the visual periphery, and gradually the faces appear to be deformed and grotesque. In this functional magnetic resonance imaging (fMRI) study, participants were presented with four conditions: (a) one face pair changing only its illumination; (b) two and (c) three alternating face pairs; and (d) nonrepeated face pairs. Participants rated the magnitude of each illusion immediately after each block. Results showed that the receptive region of the early visual cortex (V1-V4), and category-selective areas such as the fusiform face area (FFA) and occipital face area (OFA), responded proportionally to the participants' rated FFD strength. A random-effects voxelwise analysis further revealed positively correlated areas (including the medial and superolateral frontal areas) and negatively correlated areas (including the precuneus, postcentral gyrus, right insula, and bilateral middle frontal gyri) with respect to participants' ratings. Time series correlations among these nine ROIs (four positive and five negative) indicated that most participants showed a clustering of the two separate ROI types. Exploratory factor analysis (EFA) also demonstrated the segregation of the positive and negative ROIs; additionally, two subsystems were identified within the negative ROIs. These results suggest that the FFD is mediated by at least two networks: one that is likely responsible for perception and another that is likely responsible for subjective feelings and engagement. © 2014 ARVO.

Brain regions sensitive to the face inversion effect: a functional magnetic resonance imaging study in humans.

PubMed

Leube, Dirk T; Yoon, Hyo Woon; Rapp, Alexander; Erb, Michael; Grodd, Wolfgang; Bartels, Mathias; Kircher, Tilo T J

2003-05-22

Perception of upright faces relies on configural processing. Therefore recognition of inverted, compared to upright faces is impaired. In a functional magnetic resonance imaging experiment we investigated the neural correlate of a face inversion task. Thirteen healthy subjects were presented with a equal number of upright and inverted faces alternating with a low level baseline with an upright and inverted picture of an abstract symbol. Brain activation was calculated for upright minus inverted faces. For this differential contrast, we found a signal change in the right superior temporal sulcus and right insula. Configural properties are processed in a network comprising right superior temporal and insular cortex.

Game Face

ERIC Educational Resources Information Center

Weiner, Jill

2005-01-01

In this article, the author discusses "Game Face: Life Lessons Across the Curriculum", a teaching kit that challenges assumptions and builds confidence. Game Face, which is derived from a book and art exhibition, "Game Face: What Does a Female Athlete Look Like?", uses layered and powerful images of women and girls participating in sports to teach…

Portraits made to measure: manipulating social judgments about individuals with a statistical face model.

PubMed

Walker, Mirella; Vetter, Thomas

2009-10-13

The social judgments people make on the basis of the facial appearance of strangers strongly affect their behavior in different contexts. However, almost nothing is known about the physical information underlying these judgments. In this article, we present a new technology (a) to quantify the information in faces that is used for social judgments and (b) to manipulate the image of a human face in a way which is almost imperceptible but changes the personality traits ascribed to the depicted person. This method was developed in a high-dimensional face space by identifying vectors that capture maximum variability in judgments of personality traits. Our method of manipulating the salience of these vectors in faces was successfully transferred to novel photographs from an independent database. We evaluated this method by showing pairs of face photographs which differed only in the salience of one of six personality traits. Subjects were asked to decide which face was more extreme with respect to the trait in question. Results show that the image manipulation produced the intended attribution effect. All response accuracies were significantly above chance level. This approach to understanding and manipulating how a person is socially perceived could be useful in psychological research and could also be applied in advertising or the film industries.

Image Reference Database in Teleradiology: Migrating to WWW

NASA Astrophysics Data System (ADS)

Pasqui, Valdo

The paper presents a multimedia Image Reference Data Base (IRDB) used in Teleradiology. The application was developed at the University of Florence in the framework of the European Community TELEMED Project. TELEMED overall goals and IRDB requirements are outlined and the resulting architecture is described. IRDB is a multisite database containing radiological images, selected because their scientific interest, and their related information. The architecture consists of a set of IRDB Installations which are accessed from Viewing Stations (VS) located at different medical sites. The interaction between VS and IRDB Installations follows the client-server paradigm and uses an OSI level-7 protocol, named Telemed Communication Language. After reviewing Florence prototype implementation and experimentation, IRDB migration to World Wide Web (WWW) is discussed. A possible scenery to implement IRDB on the basis of WWW model is depicted in order to exploit WWW servers and browsers capabilities. Finally, the advantages of this conversion are outlined.

Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability.

PubMed

Dudding-Byth, Tracy; Baxter, Anne; Holliday, Elizabeth G; Hackett, Anna; O'Donnell, Sheridan; White, Susan M; Attia, John; Brunner, Han; de Vries, Bert; Koolen, David; Kleefstra, Tjitske; Ratwatte, Seshika; Riveros, Carlos; Brain, Steve; Lovell, Brian C

2017-12-19

Massively parallel genetic sequencing allows rapid testing of known intellectual disability (ID) genes. However, the discovery of novel syndromic ID genes requires molecular confirmation in at least a second or a cluster of individuals with an overlapping phenotype or similar facial gestalt. Using computer face-matching technology we report an automated approach to matching the faces of non-identical individuals with the same genetic syndrome within a database of 3681 images [1600 images of one of 10 genetic syndrome subgroups together with 2081 control images]. Using the leave-one-out method, two research questions were specified: 1) Using two-dimensional (2D) photographs of individuals with one of 10 genetic syndromes within a database of images, did the technology correctly identify more than expected by chance: i) a top match? ii) at least one match within the top five matches? or iii) at least one in the top 10 with an individual from the same syndrome subgroup? 2) Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone? The computer face-matching technology correctly identifies a top match, at least one correct match in the top five and at least one in the top 10 more than expected by chance (P < 0.00001). There was low agreement between the technology and clinicians, with higher accuracy of the technology when results were discordant (P < 0.01) for all syndromes except Kabuki syndrome. Although the accuracy of the computer face-matching technology was tested on images of individuals with known syndromic forms of intellectual disability, the results of this pilot study illustrate the potential utility of face-matching technology within deep phenotyping platforms to facilitate the interpretation of DNA sequencing data for individuals who remain undiagnosed despite testing the known developmental disorder genes.

Activity in Face-Responsive Brain Regions is Modulated by Invisible, Attended Faces: Evidence from Masked Priming

PubMed Central

Eger, Evelyn; Dolan, Raymond; Henson, Richard N.

2009-01-01

It is often assumed that neural activity in face-responsive regions of primate cortex correlates with conscious perception of faces. However, whether such activity occurs without awareness is still debated. Using functional magnetic resonance imaging (fMRI) in conjunction with a novel masked face priming paradigm, we observed neural modulations that could not be attributed to perceptual awareness. More specifically, we found reduced activity in several classic face-processing regions, including the “fusiform face area,” “occipital face area,” and superior temporal sulcus, when a face was preceded by a briefly flashed image of the same face, relative to a different face, even when 2 images of the same face differed. Importantly, unlike most previous studies, which have minimized awareness by using conditions of inattention, the present results occurred when the stimuli (the primes) were attended. By contrast, when primes were perceived consciously, in a long-lag priming paradigm, we found repetition-related activity increases in additional frontal and parietal regions. These data not only demonstrate that fMRI activity in face-responsive regions can be modulated independently of perceptual awareness, but also document where such subliminal face-processing occurs (i.e., restricted to face-responsive regions of occipital and temporal cortex) and to what extent (i.e., independent of the specific image). PMID:18400791

Face recognition: a convolutional neural-network approach.

PubMed

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

When does subliminal affective image priming influence the ability of schizophrenic patients to perceive face emotions?

PubMed

Vaina, Lucia Maria; Rana, Kunjan D; Cotos, Ionela; Li-Yang, Chen; Huang, Melissa A; Podea, Delia

2014-12-24

Deficits in face emotion perception are among the most pervasive aspects of schizophrenia impairments which strongly affects interpersonal communication and social skills. Schizophrenic patients (PSZ) and healthy control subjects (HCS) performed 2 psychophysical tasks. One, the SAFFIMAP test, was designed to determine the impact of subliminally presented affective or neutral images on the accuracy of face-expression (angry or neutral) perception. In the second test, FEP, subjects saw pictures of face-expression and were asked to rate them as angry, happy, or neutral. The following clinical scales were used to determine the acute symptoms in PSZ: Positive and Negative Syndrome (PANSS), Young Mania Rating (YMRS), Hamilton Depression (HAM-D), and Hamilton Anxiety (HAM-A). On the SAFFIMAP test, different from the HCS group, the PSZ group tended to categorize the neutral expression of test faces as angry and their response to the test-face expression was not influenced by the affective content of the primes. In PSZ, the PANSS-positive score was significantly correlated with correct perception of angry faces for aggressive or pleasant primes. YMRS scores were strongly correlated with PSZ's tendency to recognize angry face expressions when the prime was a pleasant or a neutral image. The HAM-D score was positively correlated with categorizing the test-faces as neutral, regardless of the affective content of the prime or of the test-face expression (angry or neutral). Despite its exploratory nature, this study provides the first evidence that conscious perception and categorization of facial emotions (neutral or angry) in PSZ is directly affected by their positive or negative symptoms of the disease as defined by their individual scores on the clinical diagnostic scales.

Mirror self-face perception in individuals with schizophrenia: Feelings of strangeness associated with one's own image.

PubMed

Bortolon, Catherine; Capdevielle, Delphine; Altman, Rosalie; Macgregor, Alexandra; Attal, Jérôme; Raffard, Stéphane

2017-07-01

Self-face recognition is crucial for sense of identity and for maintaining a coherent sense of self. Most of our daily life experiences with the image of our own face happen when we look at ourselves in the mirror. However, to date, mirror self-perception in schizophrenia has received little attention despite evidence that face recognition deficits and self abnormalities have been described in schizophrenia. Thus, this study aims to investigate mirror self-face perception in schizophrenia patients and its correlation with clinical symptoms. Twenty-four schizophrenia patients and twenty-five healthy controls were explicitly requested to describe their image in detail during 2min whilst looking at themselves in a mirror. Then, they were asked to report whether they experienced any self-face recognition difficulties. Results showed that schizophrenia patients reported more feelings of strangeness towards their face compared to healthy controls (U=209.5, p=0.048, r=0.28), but no statistically significant differences were found regarding misidentification (p=0.111) and failures in recognition (p=0.081). Symptoms such as hallucinations, somatic concerns and depression were also associated with self-face perception abnormalities (all p-values>0.05). Feelings of strangeness toward one's own face in schizophrenia might be part of a familiar face perception deficit or a more global self-disturbance, which is characterized by a loss of self-other boundaries and has been associated with abnormal body experiences and first rank symptoms. Regarding this last hypothesis, multisensorial integration might have an impact on the way patients perceive themselves since it has an important role in mirror self-perception. Copyright © 2017. Published by Elsevier B.V.

Faces forming traces: neurophysiological correlates of learning naturally distinctive and caricatured faces.

PubMed

Schulz, Claudia; Kaufmann, Jürgen M; Kurt, Alexander; Schweinberger, Stefan R

2012-10-15

Distinctive faces are easier to learn and recognise than typical faces. We investigated effects of natural vs. artificial distinctiveness on performance and neural correlates of face learning. Spatial caricatures of initially non-distinctive faces were created such that their rated distinctiveness matched a set of naturally distinctive faces. During learning, we presented naturally distinctive, caricatured, and non-distinctive faces for later recognition among novel faces, using different images of the same identities at learning and test. For learned faces, an advantage in performance was observed for naturally distinctive and caricatured over non-distinctive faces, with larger benefits for naturally distinctive faces. Distinctive and caricatured faces elicited more negative occipitotemporal ERPs (P200, N250) and larger centroparietal positivity (LPC) during learning. At test, earliest distinctiveness effects were again seen in the P200. In line with recent research, N250 and LPC were larger for learned than for novel faces overall. Importantly, whereas left hemispheric N250 was increased for learned naturally distinctive faces, right hemispheric N250 responded particularly to caricatured novel faces. We conclude that natural distinctiveness induces benefits to face recognition beyond those induced by exaggeration of a face's idiosyncratic shape, and that the left hemisphere in particular may mediate recognition across different images. Copyright © 2012 Elsevier Inc. All rights reserved.