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.

Novel method to predict body weight in children based on age and morphological facial features.

PubMed

Huang, Ziyin; Barrett, Jeffrey S; Barrett, Kyle; Barrett, Ryan; Ng, Chee M

2015-04-01

A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three-layer feed-forward artificial neural network (ANN) model is reported. The model takes in four parameters, including age-based CDC-inferred median body weight and three facial feature distances measured from digital facial images. In this study, thirty-nine volunteer subjects with age ranging from 6-18 years old and BW ranging from 18.6-96.4 kg were used for model development and validation. The final model has a mean prediction error of 0.48, a mean squared error of 18.43, and a coefficient of correlation of 0.94. The model shows significant improvement in prediction accuracy over several age-based body weight prediction methods. Combining with a facial recognition algorithm that can detect, extract and measure the facial features used in this study, mobile applications that incorporate this body weight prediction method may be developed for clinical investigations where access to scales is limited. © 2014, The American College of Clinical Pharmacology.

Perceived Attractiveness, Facial Features, and African Self-Consciousness.

ERIC Educational Resources Information Center

Chambers, John W., Jr.; And Others

1994-01-01

Investigated relationships between perceived attractiveness, facial features, and African self-consciousness (ASC) among 149 African American college students. As predicted, high ASC subjects used more positive adjectives in descriptions of strong African facial features than did medium or low ASC subjects. Results are discussed in the context of…

Facial expression recognition under partial occlusion based on fusion of global and local features

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Xia, Chen; Hu, Min; Ren, Fuji

2018-04-01

Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

Dynamic facial expression recognition based on geometric and texture features

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Zengfu

2018-04-01

Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

Non-invasive health status detection system using Gabor filters based on facial block texture features.

PubMed

Shu, Ting; Zhang, Bob

2015-04-01

Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks.

Feature selection from a facial image for distinction of sasang constitution.

PubMed

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

2009-09-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Selective attention to a facial feature with and without facial context: an ERP-study.

PubMed

Wijers, A A; Van Besouw, N J P; Mulder, G

2002-04-01

The present experiment addressed the question whether selectively attending to a facial feature (mouth shape) would benefit from the presence of a correct facial context. Subjects attended selectively to one of two possible mouth shapes belonging to photographs of a face with a happy or sad expression, respectively. These mouths were presented randomly either in isolation, embedded in the original photos, or in an exchanged facial context. The ERP effect of attending mouth shape was a lateral posterior negativity, anterior positivity with an onset latency of 160-200 ms; this effect was completely unaffected by the type of facial context. When the mouth shape and the facial context conflicted, this resulted in a medial parieto-occipital positivity with an onset latency of 180 ms, independent of the relevance of the mouth shape. Finally, there was a late (onset at approx. 400 ms) expression (happy vs. sad) effect, which was strongly lateralized to the right posterior hemisphere and was most prominent for attended stimuli in the correct facial context. For the isolated mouth stimuli, a similarly distributed expression effect was observed at an earlier latency range (180-240 ms). These data suggest the existence of separate, independent and neuroanatomically segregated processors engaged in the selective processing of facial features and the detection of contextual congruence and emotional expression of face stimuli. The data do not support that early selective attention processes benefit from top-down constraints provided by the correct facial context.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

PubMed Central

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

2009-01-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

Features versus context: An approach for precise and detailed detection and delineation of faces and facial features.

PubMed

Ding, Liya; Martinez, Aleix M

2010-11-01

The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide

Subject-specific and pose-oriented facial features for face recognition across poses.

PubMed

Lee, Ping-Han; Hsu, Gee-Sern; Wang, Yun-Wen; Hung, Yi-Ping

2012-10-01

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

Person-independent facial expression analysis by fusing multiscale cell features

NASA Astrophysics Data System (ADS)

Zhou, Lubing; Wang, Han

2013-03-01

Automatic facial expression recognition is an interesting and challenging task. To achieve satisfactory accuracy, deriving a robust facial representation is especially important. A novel appearance-based feature, the multiscale cell local intensity increasing patterns (MC-LIIP), to represent facial images and conduct person-independent facial expression analysis is presented. The LIIP uses a decimal number to encode the texture or intensity distribution around each pixel via pixel-to-pixel intensity comparison. To boost noise resistance, MC-LIIP carries out comparison computation on the average values of scalable cells instead of individual pixels. The facial descriptor fuses region-based histograms of MC-LIIP features from various scales, so as to encode not only textural microstructures but also the macrostructures of facial images. Finally, a support vector machine classifier is applied for expression recognition. Experimental results on the CK+ and Karolinska directed emotional faces databases show the superiority of the proposed method.

Young Children's Ability to Match Facial Features Typical of Race.

ERIC Educational Resources Information Center

Lacoste, Ronald J.

This study examined (1) the ability of 3- and 4-year-old children to racially classify Negro and Caucasian facial features in the absence of skin color as a racial cue; and (2) the relative value attached to the facial features of Negro and Caucasian races. Subjects were 21 middle income, Caucasian children from a privately owned nursery school in…

Effects of face feature and contour crowding in facial expression adaptation.

PubMed

Liu, Pan; Montaser-Kouhsari, Leila; Xu, Hong

2014-12-01

Prolonged exposure to a visual stimulus, such as a happy face, biases the perception of subsequently presented neutral face toward sad perception, the known face adaptation. Face adaptation is affected by visibility or awareness of the adapting face. However, whether it is affected by discriminability of the adapting face is largely unknown. In the current study, we used crowding to manipulate discriminability of the adapting face and test its effect on face adaptation. Instead of presenting flanking faces near the target face, we shortened the distance between facial features (internal feature crowding), and reduced the size of face contour (external contour crowding), to introduce crowding. We are interested in whether internal feature crowding or external contour crowding is more effective in inducing crowding effect in our first experiment. We found that combining internal feature and external contour crowding, but not either of them alone, induced significant crowding effect. In Experiment 2, we went on further to investigate its effect on adaptation. We found that both internal feature crowding and external contour crowding reduced its facial expression aftereffect (FEA) significantly. However, we did not find a significant correlation between discriminability of the adapting face and its FEA. Interestingly, we found a significant correlation between discriminabilities of the adapting and test faces. Experiment 3 found that the reduced adaptation aftereffect in combined crowding by the external face contour and the internal facial features cannot be decomposed into the effects from the face contour and facial features linearly. It thus suggested a nonlinear integration between facial features and face contour in face adaptation.

Interpretation of Appearance: The Effect of Facial Features on First Impressions and Personality

PubMed Central

Wolffhechel, Karin; Fagertun, Jens; Jacobsen, Ulrik Plesner; Majewski, Wiktor; Hemmingsen, Astrid Sofie; Larsen, Catrine Lohmann; Lorentzen, Sofie Katrine; Jarmer, Hanne

2014-01-01

Appearance is known to influence social interactions, which in turn could potentially influence personality development. In this study we focus on discovering the relationship between self-reported personality traits, first impressions and facial characteristics. The results reveal that several personality traits can be read above chance from a face, and that facial features influence first impressions. Despite the former, our prediction model fails to reliably infer personality traits from either facial features or first impressions. First impressions, however, could be inferred more reliably from facial features. We have generated artificial, extreme faces visualising the characteristics having an effect on first impressions for several traits. Conclusively, we find a relationship between first impressions, some personality traits and facial features and consolidate that people on average assess a given face in a highly similar manner. PMID:25233221

Interpretation of appearance: the effect of facial features on first impressions and personality.

PubMed

Wolffhechel, Karin; Fagertun, Jens; Jacobsen, Ulrik Plesner; Majewski, Wiktor; Hemmingsen, Astrid Sofie; Larsen, Catrine Lohmann; Lorentzen, Sofie Katrine; Jarmer, Hanne

2014-01-01

Appearance is known to influence social interactions, which in turn could potentially influence personality development. In this study we focus on discovering the relationship between self-reported personality traits, first impressions and facial characteristics. The results reveal that several personality traits can be read above chance from a face, and that facial features influence first impressions. Despite the former, our prediction model fails to reliably infer personality traits from either facial features or first impressions. First impressions, however, could be inferred more reliably from facial features. We have generated artificial, extreme faces visualising the characteristics having an effect on first impressions for several traits. Conclusively, we find a relationship between first impressions, some personality traits and facial features and consolidate that people on average assess a given face in a highly similar manner.

Facial feature tracking: a psychophysiological measure to assess exercise intensity?

PubMed

Miles, Kathleen H; Clark, Bradley; Périard, Julien D; Goecke, Roland; Thompson, Kevin G

2018-04-01

The primary aim of this study was to determine whether facial feature tracking reliably measures changes in facial movement across varying exercise intensities. Fifteen cyclists completed three, incremental intensity, cycling trials to exhaustion while their faces were recorded with video cameras. Facial feature tracking was found to be a moderately reliable measure of facial movement during incremental intensity cycling (intra-class correlation coefficient = 0.65-0.68). Facial movement (whole face (WF), upper face (UF), lower face (LF) and head movement (HM)) increased with exercise intensity, from lactate threshold one (LT1) until attainment of maximal aerobic power (MAP) (WF 3464 ± 3364mm, P < 0.005; UF 1961 ± 1779mm, P = 0.002; LF 1608 ± 1404mm, P = 0.002; HM 849 ± 642mm, P < 0.001). UF movement was greater than LF movement at all exercise intensities (UF minus LF at: LT1, 1048 ± 383mm; LT2, 1208 ± 611mm; MAP, 1401 ± 712mm; P < 0.001). Significant medium to large non-linear relationships were found between facial movement and power output (r 2  = 0.24-0.31), HR (r 2  = 0.26-0.33), [La - ] (r 2  = 0.33-0.44) and RPE (r 2  = 0.38-0.45). The findings demonstrate the potential utility of facial feature tracking as a non-invasive, psychophysiological measure to potentially assess exercise intensity.

Facial expression recognition based on improved deep belief networks

NASA Astrophysics Data System (ADS)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Orientations for the successful categorization of facial expressions and their link with facial features.

PubMed

Duncan, Justin; Gosselin, Frédéric; Cobarro, Charlène; Dugas, Gabrielle; Blais, Caroline; Fiset, Daniel

2017-12-01

Horizontal information was recently suggested to be crucial for face identification. In the present paper, we expand on this finding and investigate the role of orientations for all the basic facial expressions and neutrality. To this end, we developed orientation bubbles to quantify utilization of the orientation spectrum by the visual system in a facial expression categorization task. We first validated the procedure in Experiment 1 with a simple plaid-detection task. In Experiment 2, we used orientation bubbles to reveal the diagnostic-i.e., task relevant-orientations for the basic facial expressions and neutrality. Overall, we found that horizontal information was highly diagnostic for expressions-surprise excepted. We also found that utilization of horizontal information strongly predicted performance level in this task. Despite the recent surge of research on horizontals, the link with local features remains unexplored. We were thus also interested in investigating this link. In Experiment 3, location bubbles were used to reveal the diagnostic features for the basic facial expressions. Crucially, Experiments 2 and 3 were run in parallel on the same participants, in an interleaved fashion. This way, we were able to correlate individual orientation and local diagnostic profiles. Our results indicate that individual differences in horizontal tuning are best predicted by utilization of the eyes.

Recognition of children on age-different images: Facial morphology and age-stable features.

PubMed

Caplova, Zuzana; Compassi, Valentina; Giancola, Silvio; Gibelli, Daniele M; Obertová, Zuzana; Poppa, Pasquale; Sala, Remo; Sforza, Chiarella; Cattaneo, Cristina

2017-07-01

The situation of missing children is one of the most emotional social issues worldwide. The search for and identification of missing children is often hampered, among others, by the fact that the facial morphology of long-term missing children changes as they grow. Nowadays, the wide coverage by surveillance systems potentially provides image material for comparisons with images of missing children that may facilitate identification. The aim of study was to identify whether facial features are stable in time and can be utilized for facial recognition by comparing facial images of children at different ages as well as to test the possible use of moles in recognition. The study was divided into two phases (1) morphological classification of facial features using an Anthropological Atlas; (2) algorithm developed in MATLAB® R2014b for assessing the use of moles as age-stable features. The assessment of facial features by Anthropological Atlases showed high mismatch percentages among observers. On average, the mismatch percentages were lower for features describing shape than for those describing size. The nose tip cleft and the chin dimple showed the best agreement between observers regarding both categorization and stability over time. Using the position of moles as a reference point for recognition of the same person on age-different images seems to be a useful method in terms of objectivity and it can be concluded that moles represent age-stable facial features that may be considered for preliminary recognition. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Attractiveness as a Function of Skin Tone and Facial Features: Evidence from Categorization Studies.

PubMed

Stepanova, Elena V; Strube, Michael J

2018-01-01

Participants rated the attractiveness and racial typicality of male faces varying in their facial features from Afrocentric to Eurocentric and in skin tone from dark to light in two experiments. Experiment 1 provided evidence that facial features and skin tone have an interactive effect on perceptions of attractiveness and mixed-race faces are perceived as more attractive than single-race faces. Experiment 2 further confirmed that faces with medium levels of skin tone and facial features are perceived as more attractive than faces with extreme levels of these factors. Black phenotypes (combinations of dark skin tone and Afrocentric facial features) were rated as more attractive than White phenotypes (combinations of light skin tone and Eurocentric facial features); ambiguous faces (combinations of Afrocentric and Eurocentric physiognomy) with medium levels of skin tone were rated as the most attractive in Experiment 2. Perceptions of attractiveness were relatively independent of racial categorization in both experiments.

Facial Emotions Recognition using Gabor Transform and Facial Animation Parameters with Neural Networks

NASA Astrophysics Data System (ADS)

Harit, Aditya; Joshi, J. C., Col; Gupta, K. K.

2018-03-01

The paper proposed an automatic facial emotion recognition algorithm which comprises of two main components: feature extraction and expression recognition. The algorithm uses a Gabor filter bank on fiducial points to find the facial expression features. The resulting magnitudes of Gabor transforms, along with 14 chosen FAPs (Facial Animation Parameters), compose the feature space. There are two stages: the training phase and the recognition phase. Firstly, for the present 6 different emotions, the system classifies all training expressions in 6 different classes (one for each emotion) in the training stage. In the recognition phase, it recognizes the emotion by applying the Gabor bank to a face image, then finds the fiducial points, and then feeds it to the trained neural architecture.

What's in a "face file"? Feature binding with facial identity, emotion, and gaze direction.

PubMed

Fitousi, Daniel

2017-07-01

A series of four experiments investigated the binding of facial (i.e., facial identity, emotion, and gaze direction) and non-facial (i.e., spatial location and response location) attributes. Evidence for the creation and retrieval of temporary memory face structures across perception and action has been adduced. These episodic structures-dubbed herein "face files"-consisted of both visuo-visuo and visuo-motor bindings. Feature binding was indicated by partial-repetition costs. That is repeating a combination of facial features or altering them altogether, led to faster responses than repeating or alternating only one of the features. Taken together, the results indicate that: (a) "face files" affect both action and perception mechanisms, (b) binding can take place with facial dimensions and is not restricted to low-level features (Hommel, Visual Cognition 5:183-216, 1998), and (c) the binding of facial and non-facial attributes is facilitated if the dimensions share common spatial or motor codes. The theoretical contributions of these results to "person construal" theories (Freeman, & Ambady, Psychological Science, 20(10), 1183-1188, 2011), as well as to face recognition models (Haxby, Hoffman, & Gobbini, Biological Psychiatry, 51(1), 59-67, 2000) are discussed.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions.

PubMed

Shu, Ting; Zhang, Bob; Yan Tang, Yuan

2017-04-01

Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model.

PubMed

Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming; Kakadiaris, Ioannis A

2011-10-01

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.

Mutual information-based facial expression recognition

NASA Astrophysics Data System (ADS)

Hazar, Mliki; Hammami, Mohamed; Hanêne, Ben-Abdallah

2013-12-01

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

Recognizing Action Units for Facial Expression Analysis

PubMed Central

Tian, Ying-li; Kanade, Takeo; Cohn, Jeffrey F.

2010-01-01

Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AUs) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AUs and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AUs and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AUs. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams. PMID:25210210

Improved facial affect recognition in schizophrenia following an emotion intervention, but not training attention-to-facial-features or treatment-as-usual.

PubMed

Tsotsi, Stella; Kosmidis, Mary H; Bozikas, Vasilis P

2017-08-01

In schizophrenia, impaired facial affect recognition (FAR) has been associated with patients' overall social functioning. Interventions targeting attention or FAR per se have invariably yielded improved FAR performance in these patients. Here, we compared the effects of two interventions, one targeting FAR and one targeting attention-to-facial-features, with treatment-as-usual on patients' FAR performance. Thirty-nine outpatients with schizophrenia were randomly assigned to one of three groups: FAR intervention (training to recognize emotional information, conveyed by changes in facial features), attention-to-facial-features intervention (training to detect changes in facial features), and treatment-as-usual. Also, 24 healthy controls, matched for age and education, were assigned to one of the two interventions. Two FAR measurements, baseline and post-intervention, were conducted using an original experimental procedure with alternative sets of stimuli. We found improved FAR performance following the intervention targeting FAR in comparison to the other patient groups, which in fact was comparable to the pre-intervention performance of healthy controls in the corresponding intervention group. This improvement was more pronounced in recognizing fear. Our findings suggest that compared to interventions targeting attention, and treatment-as-usual, training programs targeting FAR can be more effective in improving FAR in patients with schizophrenia, particularly assisting them in perceiving threat-related information more accurately. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

NASA Astrophysics Data System (ADS)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

Pose-variant facial expression recognition using an embedded image system

NASA Astrophysics Data System (ADS)

Song, Kai-Tai; Han, Meng-Ju; Chang, Shuo-Hung

2008-12-01

In recent years, one of the most attractive research areas in human-robot interaction is automated facial expression recognition. Through recognizing the facial expression, a pet robot can interact with human in a more natural manner. In this study, we focus on the facial pose-variant problem. A novel method is proposed in this paper to recognize pose-variant facial expressions. After locating the face position in an image frame, the active appearance model (AAM) is applied to track facial features. Fourteen feature points are extracted to represent the variation of facial expressions. The distance between feature points are defined as the feature values. These feature values are sent to a support vector machine (SVM) for facial expression determination. The pose-variant facial expression is classified into happiness, neutral, sadness, surprise or anger. Furthermore, in order to evaluate the performance for practical applications, this study also built a low resolution database (160x120 pixels) using a CMOS image sensor. Experimental results show that the recognition rate is 84% with the self-built database.

Contributions of feature shapes and surface cues to the recognition of facial expressions.

PubMed

Sormaz, Mladen; Young, Andrew W; Andrews, Timothy J

2016-10-01

Theoretical accounts of face processing often emphasise feature shapes as the primary visual cue to the recognition of facial expressions. However, changes in facial expression also affect the surface properties of the face. In this study, we investigated whether this surface information can also be used in the recognition of facial expression. First, participants identified facial expressions (fear, anger, disgust, sadness, happiness) from images that were manipulated such that they varied mainly in shape or mainly in surface properties. We found that the categorization of facial expression is possible in either type of image, but that different expressions are relatively dependent on surface or shape properties. Next, we investigated the relative contributions of shape and surface information to the categorization of facial expressions. This employed a complementary method that involved combining the surface properties of one expression with the shape properties from a different expression. Our results showed that the categorization of facial expressions in these hybrid images was equally dependent on the surface and shape properties of the image. Together, these findings provide a direct demonstration that both feature shape and surface information make significant contributions to the recognition of facial expressions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Quantitative analysis of facial paralysis using local binary patterns in biomedical videos.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F; Xing, Dongshan

2009-07-01

Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Objective grading of facial paralysis using Local Binary Patterns in video processing.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F

2008-01-01

This paper presents a novel framework for objective measurement of facial paralysis in biomedial videos. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the Local Binary Patterns (LBP) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of block schemes. A multi-resolution extension of uniform LBP is proposed to efficiently combine the micro-patterns and large-scale patterns into a feature vector, which increases the algorithmic robustness and reduces noise effects while still retaining computational simplicity. The symmetry of facial movements is measured by the Resistor-Average Distance (RAD) between LBP features extracted from the two sides of the face. Support Vector Machine (SVM) is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) Scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Discrimination of gender using facial image with expression change

NASA Astrophysics Data System (ADS)

Kuniyada, Jun; Fukuda, Takahiro; Terada, Kenji

2005-12-01

By carrying out marketing research, the managers of large-sized department stores or small convenience stores obtain the information such as ratio of men and women of visitors and an age group, and improve their management plan. However, these works are carried out in the manual operations, and it becomes a big burden to small stores. In this paper, the authors propose a method of men and women discrimination by extracting difference of the facial expression change from color facial images. Now, there are a lot of methods of the automatic recognition of the individual using a motion facial image or a still facial image in the field of image processing. However, it is very difficult to discriminate gender under the influence of the hairstyle and clothes, etc. Therefore, we propose the method which is not affected by personality such as size and position of facial parts by paying attention to a change of an expression. In this method, it is necessary to obtain two facial images with an expression and an expressionless. First, a region of facial surface and the regions of facial parts such as eyes, nose, and mouth are extracted in the facial image with color information of hue and saturation in HSV color system and emphasized edge information. Next, the features are extracted by calculating the rate of the change of each facial part generated by an expression change. In the last step, the values of those features are compared between the input data and the database, and the gender is discriminated. In this paper, it experimented for the laughing expression and smile expression, and good results were provided for discriminating gender.

Faces in-between: evaluations reflect the interplay of facial features and task-dependent fluency.

PubMed

Winkielman, Piotr; Olszanowski, Michal; Gola, Mateusz

2015-04-01

Facial features influence social evaluations. For example, faces are rated as more attractive and trustworthy when they have more smiling features and also more female features. However, the influence of facial features on evaluations should be qualified by the affective consequences of fluency (cognitive ease) with which such features are processed. Further, fluency (along with its affective consequences) should depend on whether the current task highlights conflict between specific features. Four experiments are presented. In 3 experiments, participants saw faces varying in expressions ranging from pure anger, through mixed expression, to pure happiness. Perceivers first categorized faces either on a control dimension, or an emotional dimension (angry/happy). Thus, the emotional categorization task made "pure" expressions fluent and "mixed" expressions disfluent. Next, participants made social evaluations. Results show that after emotional categorization, but not control categorization, targets with mixed expressions are relatively devalued. Further, this effect is mediated by categorization disfluency. Additional data from facial electromyography reveal that on a basic physiological level, affective devaluation of mixed expressions is driven by their objective ambiguity. The fourth experiment shows that the relative devaluation of mixed faces that vary in gender ambiguity requires a gender categorization task. Overall, these studies highlight that the impact of facial features on evaluation is qualified by their fluency, and that the fluency of features is a function of the current task. The discussion highlights the implications of these findings for research on emotional reactions to ambiguity. (c) 2015 APA, all rights reserved).

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

  Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Sya'Rani Machrizzandi, M.

2018-03-01

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

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%.

Neural correlates of processing facial identity based on features versus their spacing.

PubMed

Maurer, D; O'Craven, K M; Le Grand, R; Mondloch, C J; Springer, M V; Lewis, T L; Grady, C L

2007-04-08

Adults' expertise in recognizing facial identity involves encoding subtle differences among faces in the shape of individual facial features (featural processing) and in the spacing among features (a type of configural processing called sensitivity to second-order relations). We used fMRI to investigate the neural mechanisms that differentiate these two types of processing. Participants made same/different judgments about pairs of faces that differed only in the shape of the eyes and mouth, with minimal differences in spacing (featural blocks), or pairs of faces that had identical features but differed in the positions of those features (spacing blocks). From a localizer scan with faces, objects, and houses, we identified regions with comparatively more activity for faces, including the fusiform face area (FFA) in the right fusiform gyrus, other extrastriate regions, and prefrontal cortices. Contrasts between the featural and spacing conditions revealed distributed patterns of activity differentiating the two conditions. A region of the right fusiform gyrus (near but not overlapping the localized FFA) showed greater activity during the spacing task, along with multiple areas of right frontal cortex, whereas left prefrontal activity increased for featural processing. These patterns of activity were not related to differences in performance between the two tasks. The results indicate that the processing of facial features is distinct from the processing of second-order relations in faces, and that these functions are mediated by separate and lateralized networks involving the right fusiform gyrus, although the FFA as defined from a localizer scan is not differentially involved.

A new atlas for the evaluation of facial features: advantages, limits, and applicability.

PubMed

Ritz-Timme, Stefanie; Gabriel, Peter; Obertovà, Zuzana; Boguslawski, Melanie; Mayer, F; Drabik, A; Poppa, Pasquale; De Angelis, Danilo; Ciaffi, Romina; Zanotti, Benedetta; Gibelli, Daniele; Cattaneo, Cristina

2011-03-01

Methods for the verification of the identity of offenders in cases involving video-surveillance images in criminal investigation events are currently under scrutiny by several forensic experts around the globe. The anthroposcopic, or morphological, approach based on facial features is the most frequently used by international forensic experts. However, a specific set of applicable features has not yet been agreed on by the experts. Furthermore, population frequencies of such features have not been recorded, and only few validation tests have been published. To combat and prevent crime in Europe, the European Commission funded an extensive research project dedicated to the optimization of methods for facial identification of persons on photographs. Within this research project, standardized photographs of 900 males between 20 and 31 years of age from Germany, Italy, and Lithuania were acquired. Based on these photographs, 43 facial features were described and evaluated in detail. These efforts led to the development of a new model of a morphologic atlas, called DMV atlas ("Düsseldorf Milan Vilnius," from the participating cities). This study is the first attempt at verifying the feasibility of this atlas as a preliminary step to personal identification by exploring the intra- and interobserver error. The analysis yielded mismatch percentages from 19% to 39%, which reflect the subjectivity of the approach and suggest caution in verifying personal identity only from the classification of facial features. Nonetheless, the use of the atlas leads to a significant improvement of consistency in the evaluation.

Recognizing Facial Slivers.

PubMed

Gilad-Gutnick, Sharon; Harmatz, Elia Samuel; Tsourides, Kleovoulos; Yovel, Galit; Sinha, Pawan

2018-07-01

We report here an unexpectedly robust ability of healthy human individuals ( n = 40) to recognize extremely distorted needle-like facial images, challenging the well-entrenched notion that veridical spatial configuration is necessary for extracting facial identity. In face identification tasks of parametrically compressed internal and external features, we found that the sum of performances on each cue falls significantly short of performance on full faces, despite the equal visual information available from both measures (with full faces essentially being a superposition of internal and external features). We hypothesize that this large deficit stems from the use of positional information about how the internal features are positioned relative to the external features. To test this, we systematically changed the relations between internal and external features and found preferential encoding of vertical but not horizontal spatial relationships in facial representations ( n = 20). Finally, we employ magnetoencephalography imaging ( n = 20) to demonstrate a close mapping between the behavioral psychometric curve and the amplitude of the M250 face familiarity, but not M170 face-sensitive evoked response field component, providing evidence that the M250 can be modulated by faces that are perceptually identifiable, irrespective of extreme distortions to the face's veridical configuration. We theorize that the tolerance to compressive distortions has evolved from the need to recognize faces across varying viewpoints. Our findings help clarify the important, but poorly defined, concept of facial configuration and also enable an association between behavioral performance and previously reported neural correlates of face perception.

Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

NASA Astrophysics Data System (ADS)

Kim, Jonghwa; André, Elisabeth

This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

Rigid Facial Motion Influences Featural, But Not Holistic, Face Processing

PubMed Central

Xiao, Naiqi; Quinn, Paul C.; Ge, Liezhong; Lee, Kang

2012-01-01

We report three experiments in which we investigated the effect of rigid facial motion on face processing. Specifically, we used the face composite effect to examine whether rigid facial motion influences primarily featural or holistic processing of faces. In Experiments 1, 2, and 3, participants were first familiarized with dynamic displays in which a target face turned from one side to another; then at test, participants judged whether the top half of a composite face (the top half of the target face aligned or misaligned with the bottom half of a foil face) belonged to the target face. We compared performance in the dynamic condition to various static control conditions in Experiments 1, 2, and 3, which differed from each other in terms of the display order of the multiple static images or the inter stimulus interval (ISI) between the images. We found that the size of the face composite effect in the dynamic condition was significantly smaller than that in the static conditions. In other words, the dynamic face display influenced participants to process the target faces in a part-based manner and consequently their recognition of the upper portion of the composite face at test became less interfered with by the aligned lower part of the foil face. The findings from the present experiments provide the strongest evidence to date to suggest that the rigid facial motion mainly influences facial featural, but not holistic, processing. PMID:22342561

Eruptive Facial Postinflammatory Lentigo: Clinical and Dermatoscopic Features.

PubMed

Cabrera, Raul; Puig, Susana; Larrondo, Jorge; Castro, Alex; Valenzuela, Karen; Sabatini, Natalia

2016-11-01

The face has not been considered a common site of fixed drug eruption, and the authors lack dermatoscopic studies of this condition on the subject. The authors sought to characterize clinical and dermatoscopic features of 8 cases of an eruptive facial postinflammatory lentigo. The authors conducted a retrospective review of 8 cases with similar clinical and dermatoscopic findings seen from 2 medical centers in 2 countries during 2010-2014. A total of 8 patients (2 males and 6 females) with ages that ranged from 34 to 62 years (mean: 48) presented an abrupt onset of a single facial brown-pink macule, generally asymmetrical, with an average size of 1.9 cm. after ingestion of a nonsteroidal antiinflammatory drugs that lasted for several months. Dermatoscopy mainly showed a pseudonetwork or uniform areas of brown pigmentation, brown or blue-gray dots, red dots and/or telangiectatic vessels. In the epidermis, histopathology showed a mild hydropic degeneration and focal melanin hyperpigmentation. Melanin can be found freely in the dermis or laden in macrophages along with a mild perivascular mononuclear infiltrate. The authors describe eruptive facial postinflammatory lentigo as a new variant of a fixed drug eruption on the face.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions

PubMed Central

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject’s face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject’s face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network. PMID:26859884

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

PubMed

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

Automatic recognition of emotions from facial expressions

NASA Astrophysics Data System (ADS)

Xue, Henry; Gertner, Izidor

2014-06-01

In the human-computer interaction (HCI) process it is desirable to have an artificial intelligent (AI) system that can identify and categorize human emotions from facial expressions. Such systems can be used in security, in entertainment industries, and also to study visual perception, social interactions and disorders (e.g. schizophrenia and autism). In this work we survey and compare the performance of different feature extraction algorithms and classification schemes. We introduce a faster feature extraction method that resizes and applies a set of filters to the data images without sacrificing the accuracy. In addition, we have enhanced SVM to multiple dimensions while retaining the high accuracy rate of SVM. The algorithms were tested using the Japanese Female Facial Expression (JAFFE) Database and the Database of Faces (AT&T Faces).

Recovering faces from memory: the distracting influence of external facial features.

PubMed

Frowd, Charlie D; Skelton, Faye; Atherton, Chris; Pitchford, Melanie; Hepton, Gemma; Holden, Laura; McIntyre, Alex H; Hancock, Peter J B

2012-06-01

Recognition memory for unfamiliar faces is facilitated when contextual cues (e.g., head pose, background environment, hair and clothing) are consistent between study and test. By contrast, inconsistencies in external features, especially hair, promote errors in unfamiliar face-matching tasks. For the construction of facial composites, as carried out by witnesses and victims of crime, the role of external features (hair, ears, and neck) is less clear, although research does suggest their involvement. Here, over three experiments, we investigate the impact of external features for recovering facial memories using a modern, recognition-based composite system, EvoFIT. Participant-constructors inspected an unfamiliar target face and, one day later, repeatedly selected items from arrays of whole faces, with "breeding," to "evolve" a composite with EvoFIT; further participants (evaluators) named the resulting composites. In Experiment 1, the important internal-features (eyes, brows, nose, and mouth) were constructed more identifiably when the visual presence of external features was decreased by Gaussian blur during construction: higher blur yielded more identifiable internal-features. In Experiment 2, increasing the visible extent of external features (to match the target's) in the presented face-arrays also improved internal-features quality, although less so than when external features were masked throughout construction. Experiment 3 demonstrated that masking external-features promoted substantially more identifiable images than using the previous method of blurring external-features. Overall, the research indicates that external features are a distractive rather than a beneficial cue for face construction; the results also provide a much better method to construct composites, one that should dramatically increase identification of offenders.

Characterizing facial features in individuals with craniofacial microsomia: A systematic approach for clinical research.

PubMed

Heike, Carrie L; Wallace, Erin; Speltz, Matthew L; Siebold, Babette; Werler, Martha M; Hing, Anne V; Birgfeld, Craig B; Collett, Brent R; Leroux, Brian G; Luquetti, Daniela V

2016-11-01

Craniofacial microsomia (CFM) is a congenital condition with wide phenotypic variability, including hypoplasia of the mandible and external ear. We assembled a cohort of children with facial features within the CFM spectrum and children without known craniofacial anomalies. We sought to develop a standardized approach to assess and describe the facial characteristics of the study cohort, using multiple sources of information gathered over the course of this longitudinal study and to create case subgroups with shared phenotypic features. Participants were enrolled between 1996 and 2002. We classified the facial phenotype from photographs, ratings using a modified version of the Orbital, Ear, Mandible, Nerve, Soft tissue (OMENS) pictorial system, data from medical record abstraction, and health history questionnaires. The participant sample included 142 cases and 290 controls. The average age was 13.5 years (standard deviation, 1.3 years; range, 11.1-17.1 years). Sixty-one percent of cases were male, 74% were white non-Hispanic. Among cases, the most common features were microtia (66%) and mandibular hypoplasia (50%). Case subgroups with meaningful group definitions included: (1) microtia without other CFM-related features (n = 24), (2) microtia with mandibular hypoplasia (n = 46), (3) other combinations of CFM- related facial features (n = 51), and (4) atypical features (n = 21). We developed a standardized approach for integrating multiple data sources to phenotype individuals with CFM, and created subgroups based on clinically-meaningful, shared characteristics. We hope that this system can be used to explore associations between phenotype and clinical outcomes of children with CFM and to identify the etiology of CFM. Birth Defects Research (Part A) 106:915-926, 2016.© 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Enhanced facial texture illumination normalization for face recognition.

PubMed

Luo, Yong; Guan, Ye-Peng

2015-08-01

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

Dermatoscopic features of cutaneous non-facial non-acral lentiginous growth pattern melanomas

PubMed Central

Keir, Jeff

2014-01-01

Background: The dermatoscopic features of facial lentigo maligna (LM), facial lentigo maligna melanoma (LMM) and acral lentiginous melanoma (ALM) have been well described. This is the first description of the dermatoscopic appearance of a clinical series of cutaneous non-facial non-acral lentiginous growth pattern melanomas. Objective: To describe the dermatoscopic features of a series of cutaneous non-facial non-acral lentiginous growth pattern melanomas in an Australian skin cancer practice. Method: Single observer retrospective analysis of dermatoscopic images of a one-year series of cutaneous non-facial, non-acral melanomas reported as having a lentiginous growth pattern detected in an open access primary care skin cancer clinic in Australia. Lesions were scored for presence of classical criteria for facial LM; modified pattern analysis (“Chaos and Clues”) criteria; and the presence of two novel criteria: a lentigo-like pigment pattern lacking a lentigo-like border, and large polygons. Results: 20 melanomas occurring in 14 female and 6 male patients were included. Average patient age was 64 years (range: 44–83). Lesion distribution was: trunk 35%; upper limb 40%; and lower limb 25%. The incidences of criteria identified were: asymmetry of color or pattern (100%); lentigo-like pigment pattern lacking a lentigo-like border (90%); asymmetrically pigmented follicular openings (APFO’s) (70%); grey blue structures (70%); large polygons (45%); eccentric structureless area (15%); bright white lines (5%). 20% of the lesions had only the novel criteria and/or APFO’s. Limitations: Single observer, single center retrospective study. Conclusions: Cutaneous non-facial non-acral melanomas with a lentiginous growth pattern may have none or very few traditional criteria for the diagnosis of melanoma. Criteria that are logically expected in lesions with a lentiginous growth pattern (lentigo-like pigment pattern lacking a lentigo-like border, APFO’s) and the novel

Dermatoscopic features of cutaneous non-facial non-acral lentiginous growth pattern melanomas.

PubMed

Keir, Jeff

2014-01-01

The dermatoscopic features of facial lentigo maligna (LM), facial lentigo maligna melanoma (LMM) and acral lentiginous melanoma (ALM) have been well described. This is the first description of the dermatoscopic appearance of a clinical series of cutaneous non-facial non-acral lentiginous growth pattern melanomas. To describe the dermatoscopic features of a series of cutaneous non-facial non-acral lentiginous growth pattern melanomas in an Australian skin cancer practice. Single observer retrospective analysis of dermatoscopic images of a one-year series of cutaneous non-facial, non-acral melanomas reported as having a lentiginous growth pattern detected in an open access primary care skin cancer clinic in Australia. Lesions were scored for presence of classical criteria for facial LM; modified pattern analysis ("Chaos and Clues") criteria; and the presence of two novel criteria: a lentigo-like pigment pattern lacking a lentigo-like border, and large polygons. 20 melanomas occurring in 14 female and 6 male patients were included. Average patient age was 64 years (range: 44-83). Lesion distribution was: trunk 35%; upper limb 40%; and lower limb 25%. The incidences of criteria identified were: asymmetry of color or pattern (100%); lentigo-like pigment pattern lacking a lentigo-like border (90%); asymmetrically pigmented follicular openings (APFO's) (70%); grey blue structures (70%); large polygons (45%); eccentric structureless area (15%); bright white lines (5%). 20% of the lesions had only the novel criteria and/or APFO's. Single observer, single center retrospective study. Cutaneous non-facial non-acral melanomas with a lentiginous growth pattern may have none or very few traditional criteria for the diagnosis of melanoma. Criteria that are logically expected in lesions with a lentiginous growth pattern (lentigo-like pigment pattern lacking a lentigo-like border, APFO's) and the novel criterion of large polygons may be useful in increasing sensitivity and

Long-term assessment of facial features and functions needing more attention in treatment of Treacher Collins syndrome.

PubMed

Plomp, Raul G; Versnel, Sarah L; van Lieshout, Manouk J S; Poublon, Rene M L; Mathijssen, Irene M J

2013-08-01

This study aimed to determine which facial features and functions need more attention during surgical treatment of Treacher Collins syndrome (TCS) in the long term. A cross-sectional cohort study was conducted to compare 23 TCS patients with 206 controls (all≥18 years) regarding satisfaction with their face. The adjusted Body Cathexis Scale was used to determine satisfaction with the appearance of the different facial features and functions. Desire for further treatment of these items was questioned. For each patient an overview was made of all facial operations performed, the affected facial features and the objective severity of the facial deformities. Patients were least satisfied with the appearance of the ears, facial profile and eyelids and with the functions hearing and nasal patency (P<0.001). Residual deformity of the reconstructed facial areas remained a problem in mainly the orbital area. The desire for further treatment and dissatisfaction was high in the operated patients, predominantly for eyelid reconstructions. Another significant wish was for improvement of hearing. In patients with TCS, functional deficits of the face are shown to be as important as the facial appearance. Particularly nasal patency and hearing are frequently impaired and require routine screening and treatment from intake onwards. Furthermore, correction of ear deformities and midface hypoplasia should be offered and performed more frequently. Residual deformity and dissatisfaction remains a problem, especially in reconstructed eyelids. II. Copyright © 2013 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Facial Features: What Women Perceive as Attractive and What Men Consider Attractive

PubMed Central

Muñoz-Reyes, José Antonio; Iglesias-Julios, Marta; Pita, Miguel; Turiegano, Enrique

2015-01-01

Attractiveness plays an important role in social exchange and in the ability to attract potential mates, especially for women. Several facial traits have been described as reliable indicators of attractiveness in women, but very few studies consider the influence of several measurements simultaneously. In addition, most studies consider just one of two assessments to directly measure attractiveness: either self-evaluation or men's ratings. We explored the relationship between these two estimators of attractiveness and a set of facial traits in a sample of 266 young Spanish women. These traits are: facial fluctuating asymmetry, facial averageness, facial sexual dimorphism, and facial maturity. We made use of the advantage of having recently developed methodologies that enabled us to measure these variables in real faces. We also controlled for three other widely used variables: age, body mass index and waist-to-hip ratio. The inclusion of many different variables allowed us to detect any possible interaction between the features described that could affect attractiveness perception. Our results show that facial fluctuating asymmetry is related both to self-perceived and male-rated attractiveness. Other facial traits are related only to one direct attractiveness measurement: facial averageness and facial maturity only affect men's ratings. Unmodified faces are closer to natural stimuli than are manipulated photographs, and therefore our results support the importance of employing unmodified faces to analyse the factors affecting attractiveness. We also discuss the relatively low equivalence between self-perceived and male-rated attractiveness and how various anthropometric traits are relevant to them in different ways. Finally, we highlight the need to perform integrated-variable studies to fully understand female attractiveness. PMID:26161954

Facial Features: What Women Perceive as Attractive and What Men Consider Attractive.

PubMed

Muñoz-Reyes, José Antonio; Iglesias-Julios, Marta; Pita, Miguel; Turiegano, Enrique

2015-01-01

Attractiveness plays an important role in social exchange and in the ability to attract potential mates, especially for women. Several facial traits have been described as reliable indicators of attractiveness in women, but very few studies consider the influence of several measurements simultaneously. In addition, most studies consider just one of two assessments to directly measure attractiveness: either self-evaluation or men's ratings. We explored the relationship between these two estimators of attractiveness and a set of facial traits in a sample of 266 young Spanish women. These traits are: facial fluctuating asymmetry, facial averageness, facial sexual dimorphism, and facial maturity. We made use of the advantage of having recently developed methodologies that enabled us to measure these variables in real faces. We also controlled for three other widely used variables: age, body mass index and waist-to-hip ratio. The inclusion of many different variables allowed us to detect any possible interaction between the features described that could affect attractiveness perception. Our results show that facial fluctuating asymmetry is related both to self-perceived and male-rated attractiveness. Other facial traits are related only to one direct attractiveness measurement: facial averageness and facial maturity only affect men's ratings. Unmodified faces are closer to natural stimuli than are manipulated photographs, and therefore our results support the importance of employing unmodified faces to analyse the factors affecting attractiveness. We also discuss the relatively low equivalence between self-perceived and male-rated attractiveness and how various anthropometric traits are relevant to them in different ways. Finally, we highlight the need to perform integrated-variable studies to fully understand female attractiveness.

Variation of facial features among three African populations: Body height match analyses.

PubMed

Taura, M G; Adamu, L H; Gudaji, A

2017-01-01

Body height is one of the variables that show a correlation with facial craniometry. Here we seek to discriminate the three populations (Nigerians, Ugandans and Kenyans) using facial craniometry based on different categories of body height of adult males. A total of 513 individuals comprising 234 Nigerians, 169 Ugandans and 110 Kenyans with mean age of 25.27, s=5.13 (18-40 years) participated. Paired and unpaired facial features were measured using direct craniometry. Multivariate and stepwise discriminate function analyses were used for differentiation of the three populations. The result showed significant overall facial differences among the three populations in all the body height categories. Skull height, total facial height, outer canthal distance, exophthalmometry, right ear width and nasal length were significantly different among the three different populations irrespective of body height categories. Other variables were sensitive to body height. Stepwise discriminant function analyses included maximum of six variables for better discrimination between the three populations. The single best discriminator of the groups was total facial height, however, for body height >1.70m the single best discriminator was nasal length. Most of the variables were better used with function 1, hence, better discrimination than function 2. In conclusion, adult body height in addition to other factors such as age, sex, and ethnicity should be considered in making decision on facial craniometry. However, not all the facial linear dimensions were sensitive to body height. Copyright © 2016 Elsevier GmbH. All rights reserved.

Does my face FIT?: a face image task reveals structure and distortions of facial feature representation.

PubMed

Fuentes, Christina T; Runa, Catarina; Blanco, Xenxo Alvarez; Orvalho, Verónica; Haggard, Patrick

2013-01-01

Despite extensive research on face perception, few studies have investigated individuals' knowledge about the physical features of their own face. In this study, 50 participants indicated the location of key features of their own face, relative to an anchor point corresponding to the tip of the nose, and the results were compared to the true location of the same individual's features from a standardised photograph. Horizontal and vertical errors were analysed separately. An overall bias to underestimate vertical distances revealed a distorted face representation, with reduced face height. Factor analyses were used to identify separable subconfigurations of facial features with correlated localisation errors. Independent representations of upper and lower facial features emerged from the data pattern. The major source of variation across individuals was in representation of face shape, with a spectrum from tall/thin to short/wide representation. Visual identification of one's own face is excellent, and facial features are routinely used for establishing personal identity. However, our results show that spatial knowledge of one's own face is remarkably poor, suggesting that face representation may not contribute strongly to self-awareness.

The shape of facial features and the spacing among them generate similar inversion effects: a reply to Rossion (2008).

PubMed

Yovel, Galit

2009-11-01

It is often argued that picture-plane face inversion impairs discrimination of the spacing among face features to a greater extent than the identity of the facial features. However, several recent studies have reported similar inversion effects for both types of face manipulations. In a recent review, Rossion (2008) claimed that similar inversion effects for spacing and features are due to methodological and conceptual shortcomings and that data still support the idea that inversion impairs the discrimination of features less than that of the spacing among them. Here I will claim that when facial features differ primarily in shape, the effect of inversion on features is not smaller than the one on spacing. It is when color/contrast information is added to facial features that the inversion effect on features decreases. This obvious observation accounts for the discrepancy in the literature and suggests that the large inversion effect that was found for features that differ in shape is not a methodological artifact. These findings together with other data that are discussed are consistent with the idea that the shape of facial features and the spacing among them are integrated rather than dissociated in the holistic representation of faces.

2D/3D facial feature extraction

NASA Astrophysics Data System (ADS)

Çinar Akakin, Hatice; Ali Salah, Albert; Akarun, Lale; Sankur, Bülent

2006-02-01

We propose and compare three different automatic landmarking methods for near-frontal faces. The face information is provided as 480x640 gray-level images in addition to the corresponding 3D scene depth information. All three methods follow a coarse-to-fine suite and use the 3D information in an assist role. The first method employs a combination of principal component analysis (PCA) and independent component analysis (ICA) features to analyze the Gabor feature set. The second method uses a subset of DCT coefficients for template-based matching. These two methods employ SVM classifiers with polynomial kernel functions. The third method uses a mixture of factor analyzers to learn Gabor filter outputs. We contrast the localization performance separately with 2D texture and 3D depth information. Although the 3D depth information per se does not perform as well as texture images in landmark localization, the 3D information has still a beneficial role in eliminating the background and the false alarms.

Is the emotion recognition deficit associated with frontotemporal dementia caused by selective inattention to diagnostic facial features?

PubMed

Oliver, Lindsay D; Virani, Karim; Finger, Elizabeth C; Mitchell, Derek G V

2014-07-01

Frontotemporal dementia (FTD) is a debilitating neurodegenerative disorder characterized by severely impaired social and emotional behaviour, including emotion recognition deficits. Though fear recognition impairments seen in particular neurological and developmental disorders can be ameliorated by reallocating attention to critical facial features, the possibility that similar benefits can be conferred to patients with FTD has yet to be explored. In the current study, we examined the impact of presenting distinct regions of the face (whole face, eyes-only, and eyes-removed) on the ability to recognize expressions of anger, fear, disgust, and happiness in 24 patients with FTD and 24 healthy controls. A recognition deficit was demonstrated across emotions by patients with FTD relative to controls. Crucially, removal of diagnostic facial features resulted in an appropriate decline in performance for both groups; furthermore, patients with FTD demonstrated a lack of disproportionate improvement in emotion recognition accuracy as a result of isolating critical facial features relative to controls. Thus, unlike some neurological and developmental disorders featuring amygdala dysfunction, the emotion recognition deficit observed in FTD is not likely driven by selective inattention to critical facial features. Patients with FTD also mislabelled negative facial expressions as happy more often than controls, providing further evidence for abnormalities in the representation of positive affect in FTD. This work suggests that the emotional expression recognition deficit associated with FTD is unlikely to be rectified by adjusting selective attention to diagnostic features, as has proven useful in other select disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.

Experience improves feature extraction in Drosophila.

PubMed

Peng, Yueqing; Xi, Wang; Zhang, Wei; Zhang, Ke; Guo, Aike

2007-05-09

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

Artistic shaping of key facial features in children and adolescents.

PubMed

Sullivan, P K; Singer, D P

2001-12-01

Facial aesthetics can be enhanced by otoplasty, rhinoplasty and genioplasty. Excellent outcomes can be obtained given appropriate timing, patient selection, preoperative planning, and artistic sculpting of the region with the appropriate surgical technique. Choosing a patient with mature psychological, developmental, and anatomic features that are amenable to treatment in the pediatric population can be challenging, yet rewarding.

Recovering Faces from Memory: The Distracting Influence of External Facial Features

ERIC Educational Resources Information Center

Frowd, Charlie D.; Skelton, Faye; Atherton, Chris; Pitchford, Melanie; Hepton, Gemma; Holden, Laura; McIntyre, Alex H.; Hancock, Peter J. B.

2012-01-01

Recognition memory for unfamiliar faces is facilitated when contextual cues (e.g., head pose, background environment, hair and clothing) are consistent between study and test. By contrast, inconsistencies in external features, especially hair, promote errors in unfamiliar face-matching tasks. For the construction of facial composites, as carried…

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Analysis of differences between Western and East-Asian faces based on facial region segmentation and PCA for facial expression recognition

NASA Astrophysics Data System (ADS)

Benitez-Garcia, Gibran; Nakamura, Tomoaki; Kaneko, Masahide

2017-01-01

Darwin was the first one to assert that facial expressions are innate and universal, which are recognized across all cultures. However, recent some cross-cultural studies have questioned this assumed universality. Therefore, this paper presents an analysis of the differences between Western and East-Asian faces of the six basic expressions (anger, disgust, fear, happiness, sadness and surprise) focused on three individual facial regions of eyes-eyebrows, nose and mouth. The analysis is conducted by applying PCA for two feature extraction methods: appearance-based by using the pixel intensities of facial parts, and geometric-based by handling 125 feature points from the face. Both methods are evaluated using 4 standard databases for both racial groups and the results are compared with a cross-cultural human study applied to 20 participants. Our analysis reveals that differences between Westerns and East-Asians exist mainly on the regions of eyes-eyebrows and mouth for expressions of fear and disgust respectively. This work presents important findings for a better design of automatic facial expression recognition systems based on the difference between two racial groups.

Alagille syndrome in a Vietnamese cohort: mutation analysis and assessment of facial features.

PubMed

Lin, Henry C; Le Hoang, Phuc; Hutchinson, Anne; Chao, Grace; Gerfen, Jennifer; Loomes, Kathleen M; Krantz, Ian; Kamath, Binita M; Spinner, Nancy B

2012-05-01

Alagille syndrome (ALGS, OMIM #118450) is an autosomal dominant disorder that affects multiple organ systems including the liver, heart, eyes, vertebrae, and face. ALGS is caused by mutations in one of two genes in the Notch Signaling Pathway, Jagged1 (JAG1) or NOTCH2. In this study, analysis of 21 Vietnamese ALGS individuals led to the identification of 19 different mutations (18 JAG1 and 1 NOTCH2), 17 of which are novel, including the third reported NOTCH2 mutation in Alagille Syndrome. The spectrum of JAG1 mutations in the Vietnamese patients is similar to that previously reported, including nine frameshift, three missense, two splice site, one nonsense, two whole gene, and one partial gene deletion. The missense mutations are all likely to be disease causing, as two are loss of cysteines (C22R and C78G) and the third creates a cryptic splice site in exon 9 (G386R). No correlation between genotype and phenotype was observed. Assessment of clinical phenotype revealed that skeletal manifestations occur with a higher frequency than in previously reported Alagille cohorts. Facial features were difficult to assess and a Vietnamese pediatric gastroenterologist was only able to identify the facial phenotype in 61% of the cohort. To assess the agreement among North American dysmorphologists at detecting the presence of ALGS facial features in the Vietnamese patients, 37 clinical dysmorphologists evaluated a photographic panel of 20 Vietnamese children with and without ALGS. The dysmorphologists were unable to identify the individuals with ALGS in the majority of cases, suggesting that evaluation of facial features should not be used in the diagnosis of ALGS in this population. This is the first report of mutations and phenotypic spectrum of ALGS in a Vietnamese population. Copyright © 2012 Wiley Periodicals, Inc.

Metric and morphological assessment of facial features: a study on three European populations.

PubMed

Ritz-Timme, S; Gabriel, P; Tutkuviene, J; Poppa, P; Obertová, Z; Gibelli, D; De Angelis, D; Ratnayake, M; Rizgeliene, R; Barkus, A; Cattaneo, C

2011-04-15

Identification from video surveillance systems is becoming more and more frequent in the forensic practice. In this field, different techniques have been improved such as height estimation and gait analysis. However, the most natural approach for identifying a person in everyday life is based on facial characteristics. Scientifically, faces can be described using morphological and metric assessment of facial features. The morphological approach is largely affected by the subjective opinion of the observer, which can be mitigated by the application of descriptive atlases. In addition, this approach requires one to investigate which are the most common and rare facial characteristics in different populations. For the metric approach further studies are necessary in order to point out possible metric differences within and between different populations. The acquisition of statistically adequate population data may provide useful information for the reconstruction of biological profiles of unidentified individuals, particularly concerning ethnic affiliation, and possibly also for personal identification. This study presents the results of the morphological and metric assessment of the head and face of 900 male subjects between 20 and 31 years from Italy, Germany and Lithuania. The evaluation of the morphological traits was performed using the DMV atlas with 43 pre-defined facial characteristics. The frequencies of the types of facial features were calculated for each population in order to establish the rarest characteristics which may be used for the purpose of a biological profile and consequently for personal identification. Metric analysis performed in vivo included 24 absolute measurements and 24 indices of the head and face, including body height and body weight. The comparison of the frequencies of morphological facial features showed many similarities between the samples from Germany, Italy and Lithuania. However, several characteristics were rare or

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.

Adult preferences for infantile facial features: an ethological approach.

PubMed

Sternglanz, S H; Gray, J L; Murakami, M

1977-02-01

In 1943 Konrad Lorenz postulated that certain infantile cues served as releasers for caretaking behaviour in human adults. This study is an attempt to confirm this hypothesis and to identify relevant cues. The stimuli studied were variations in facial features, and the responses were ratings of the attractiveness of the resultant infant faces. Parametric variations of eye height, eye width, eye height and width, iris size, and vertical variations in feature position (all presented in full-face drawings) were tested for their effect on the ratings, and highly significant preferences for particular stimuli were found. In general these preferences are consistent across a wide variety of environmental factors such as social class and experience with children. These findings are consistent with an ethological interpretation of the data.

Developmental Change in Infant Categorization: The Perception of Correlations among Facial Features.

ERIC Educational Resources Information Center

Younger, Barbara

1992-01-01

Tested 7 and 10 month olds for perception of correlations among facial features. After habituation to faces displaying a pattern of correlation, 10 month olds generalized to a novel face that preserved the pattern of correlation but showed increased attention to a novel face that violated the pattern. (BC)

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

PubMed

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier.

PubMed

Barbosa, Jocelyn; Lee, Kyubum; Lee, Sunwon; Lodhi, Bilal; Cho, Jae-Gu; Seo, Woo-Keun; Kang, Jaewoo

2016-03-12

Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician's judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman's algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features' segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree

Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications.

PubMed

Parks, Connie L; Monson, Keith L

2017-04-01

The recognizability of facial images extracted from publically available medical scans raises patient privacy concerns. This study examined how accurately facial images extracted from computed tomography (CT) scans are objectively matched with corresponding photographs of the scanned individuals. The test subjects were 128 adult Americans ranging in age from 18 to 60 years, representing both sexes and three self-identified population (ancestral descent) groups (African, European, and Hispanic). Using facial recognition software, the 2D images of the extracted facial models were compared for matches against five differently sized photo galleries. Depending on the scanning protocol and gallery size, in 6-61 % of the cases, a correct life photo match for a CT-derived facial image was the top ranked image in the generated candidate lists, even when blind searching in excess of 100,000 images. In 31-91 % of the cases, a correct match was located within the top 50 images. Few significant differences (p > 0.05) in match rates were observed between the sexes or across the three age cohorts. Highly significant differences (p < 0.01) were, however, observed across the three ancestral cohorts and between the two CT scanning protocols. Results suggest that the probability of a match between a facial image extracted from a medical scan and a photograph of the individual is moderately high. The facial image data inherent in commonly employed medical imaging modalities may need to consider a potentially identifiable form of "comparable" facial imagery and protected as such under patient privacy legislation.

Orientation-sensitivity to facial features explains the Thatcher illusion.

PubMed

Psalta, Lilia; Young, Andrew W; Thompson, Peter; Andrews, Timothy J

2014-10-09

The Thatcher illusion provides a compelling example of the perceptual cost of face inversion. The Thatcher illusion is often thought to result from a disruption to the processing of spatial relations between face features. Here, we show the limitations of this account and instead demonstrate that the effect of inversion in the Thatcher illusion is better explained by a disruption to the processing of purely local facial features. Using a matching task, we found that participants were able to discriminate normal and Thatcherized versions of the same face when they were presented in an upright orientation, but not when the images were inverted. Next, we showed that the effect of inversion was also apparent when only the eye region or only the mouth region was visible. These results demonstrate that a key component of the Thatcher illusion is to be found in orientation-specific encoding of the expressive features (eyes and mouth) of the face. © 2014 ARVO.

Implicit Binding of Facial Features During Change Blindness

PubMed Central

Lyyra, Pessi; Mäkelä, Hanna; Hietanen, Jari K.; Astikainen, Piia

2014-01-01

Change blindness refers to the inability to detect visual changes if introduced together with an eye-movement, blink, flash of light, or with distracting stimuli. Evidence of implicit detection of changed visual features during change blindness has been reported in a number of studies using both behavioral and neurophysiological measurements. However, it is not known whether implicit detection occurs only at the level of single features or whether complex organizations of features can be implicitly detected as well. We tested this in adult humans using intact and scrambled versions of schematic faces as stimuli in a change blindness paradigm while recording event-related potentials (ERPs). An enlargement of the face-sensitive N170 ERP component was observed at the right temporal electrode site to changes from scrambled to intact faces, even if the participants were not consciously able to report such changes (change blindness). Similarly, the disintegration of an intact face to scrambled features resulted in attenuated N170 responses during change blindness. Other ERP deflections were modulated by changes, but unlike the N170 component, they were indifferent to the direction of the change. The bidirectional modulation of the N170 component during change blindness suggests that implicit change detection can also occur at the level of complex features in the case of facial stimuli. PMID:24498165

Implicit binding of facial features during change blindness.

PubMed

Lyyra, Pessi; Mäkelä, Hanna; Hietanen, Jari K; Astikainen, Piia

2014-01-01

Change blindness refers to the inability to detect visual changes if introduced together with an eye-movement, blink, flash of light, or with distracting stimuli. Evidence of implicit detection of changed visual features during change blindness has been reported in a number of studies using both behavioral and neurophysiological measurements. However, it is not known whether implicit detection occurs only at the level of single features or whether complex organizations of features can be implicitly detected as well. We tested this in adult humans using intact and scrambled versions of schematic faces as stimuli in a change blindness paradigm while recording event-related potentials (ERPs). An enlargement of the face-sensitive N170 ERP component was observed at the right temporal electrode site to changes from scrambled to intact faces, even if the participants were not consciously able to report such changes (change blindness). Similarly, the disintegration of an intact face to scrambled features resulted in attenuated N170 responses during change blindness. Other ERP deflections were modulated by changes, but unlike the N170 component, they were indifferent to the direction of the change. The bidirectional modulation of the N170 component during change blindness suggests that implicit change detection can also occur at the level of complex features in the case of facial stimuli.

Pressure Bearing Device Affects Extraction Socket Remodeling of Maxillary Anterior Tooth. A Prospective Clinical Trial.

PubMed

Jiang, Xi; Zhang, Yu; Chen, Bo; Lin, Ye

2017-04-01

Extraction socket remodeling and ridge preservation strategies have been extensively explored. To evaluate the efficacy of applying a micro-titanium stent as a pressure bearing device on extraction socket remodeling of maxillary anterior tooth. Twenty-four patients with a extraction socket of maxillary incisor were treated with spontaneous healing (control group) or by applying a micro-titanium stent as a facial pressure bearing device over the facial bone wall (test group). Two virtual models obtained from cone beam computed tomography data before extraction and 4 months after healing were 3-dimenionally superimposed. Facial bone wall resorption, extraction socket remodeling features and ridge width preservation rate were determined and compared between the groups. Thin facial bone wall resulted in marked resorption in both groups. The greatest palatal shifting distance of facial bone located at the coronal level in the control group, but middle level in the test group. Compared with the original extraction socket, 87.61 ± 5.88% ridge width was preserved in the test group and 55.09 ± 14.46% in the control group. Due to the facial pressure bearing property, the rigid micro-titanium stent might preserve the ridge width and alter the resorption features of extraction socket. © 2016 Wiley Periodicals, Inc.

Information based universal feature extraction

NASA Astrophysics Data System (ADS)

Amiri, Mohammad; Brause, Rüdiger

2015-02-01

In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. The goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. This will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

Responses in the right posterior superior temporal sulcus show a feature-based response to facial expression.

PubMed

Flack, Tessa R; Andrews, Timothy J; Hymers, Mark; Al-Mosaiwi, Mohammed; Marsden, Samuel P; Strachan, James W A; Trakulpipat, Chayanit; Wang, Liang; Wu, Tian; Young, Andrew W

2015-08-01

The face-selective region of the right posterior superior temporal sulcus (pSTS) plays an important role in analysing facial expressions. However, it is less clear how facial expressions are represented in this region. In this study, we used the face composite effect to explore whether the pSTS contains a holistic or feature-based representation of facial expression. Aligned and misaligned composite images were created from the top and bottom halves of faces posing different expressions. In Experiment 1, participants performed a behavioural matching task in which they judged whether the top half of two images was the same or different. The ability to discriminate the top half of the face was affected by changes in the bottom half of the face when the images were aligned, but not when they were misaligned. This shows a holistic behavioural response to expression. In Experiment 2, we used fMR-adaptation to ask whether the pSTS has a corresponding holistic neural representation of expression. Aligned or misaligned images were presented in blocks that involved repeating the same image or in which the top or bottom half of the images changed. Increased neural responses were found in the right pSTS regardless of whether the change occurred in the top or bottom of the image, showing that changes in expression were detected across all parts of the face. However, in contrast to the behavioural data, the pattern did not differ between aligned and misaligned stimuli. This suggests that the pSTS does not encode facial expressions holistically. In contrast to the pSTS, a holistic pattern of response to facial expression was found in the right inferior frontal gyrus (IFG). Together, these results suggest that pSTS reflects an early stage in the processing of facial expression in which facial features are represented independently. Copyright © 2015 Elsevier Ltd. All rights reserved.

Millennial Filipino Student Engagement Analyzer Using Facial Feature Classification

NASA Astrophysics Data System (ADS)

Manseras, R.; Eugenio, F.; Palaoag, T.

2018-03-01

Millennials has been a word of mouth of everybody and a target market of various companies nowadays. In the Philippines, they comprise one third of the total population and most of them are still in school. Having a good education system is important for this generation to prepare them for better careers. And a good education system means having quality instruction as one of the input component indicators. In a classroom environment, teachers use facial features to measure the affect state of the class. Emerging technologies like Affective Computing is one of today’s trends to improve quality instruction delivery. This, together with computer vision, can be used in analyzing affect states of the students and improve quality instruction delivery. This paper proposed a system of classifying student engagement using facial features. Identifying affect state, specifically Millennial Filipino student engagement, is one of the main priorities of every educator and this directed the authors to develop a tool to assess engagement percentage. Multiple face detection framework using Face API was employed to detect as many student faces as possible to gauge current engagement percentage of the whole class. The binary classifier model using Support Vector Machine (SVM) was primarily set in the conceptual framework of this study. To achieve the most accuracy performance of this model, a comparison of SVM to two of the most widely used binary classifiers were tested. Results show that SVM bested RandomForest and Naive Bayesian algorithms in most of the experiments from the different test datasets.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Iris recognition based on key image feature extraction.

PubMed

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

2008-01-01

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

A Robust Shape Reconstruction Method for Facial Feature Point Detection.

PubMed

Tan, Shuqiu; Chen, Dongyi; Guo, Chenggang; Huang, Zhiqi

2017-01-01

Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

Automated facial recognition of manually generated clay facial approximations: Potential application in unidentified persons data repositories.

PubMed

Parks, Connie L; Monson, Keith L

2018-01-01

This research examined how accurately 2D images (i.e., photographs) of 3D clay facial approximations were matched to corresponding photographs of the approximated individuals using an objective automated facial recognition system. Irrespective of search filter (i.e., blind, sex, or ancestry) or rank class (R 1 , R 10 , R 25 , and R 50 ) employed, few operationally informative results were observed. In only a single instance of 48 potential match opportunities was a clay approximation matched to a corresponding life photograph within the top 50 images (R 50 ) of a candidate list, even with relatively small gallery sizes created from the application of search filters (e.g., sex or ancestry search restrictions). Increasing the candidate lists to include the top 100 images (R 100 ) resulted in only two additional instances of correct match. Although other untested variables (e.g., approximation method, 2D photographic process, and practitioner skill level) may have impacted the observed results, this study suggests that 2D images of manually generated clay approximations are not readily matched to life photos by automated facial recognition systems. Further investigation is necessary in order to identify the underlying cause(s), if any, of the poor recognition results observed in this study (e.g., potential inferior facial feature detection and extraction). Additional inquiry exploring prospective remedial measures (e.g., stronger feature differentiation) is also warranted, particularly given the prominent use of clay approximations in unidentified persons casework. Copyright © 2017. Published by Elsevier B.V.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Automated Video Based Facial Expression Analysis of Neuropsychiatric Disorders

PubMed Central

Wang, Peng; Barrett, Frederick; Martin, Elizabeth; Milanova, Marina; Gur, Raquel E.; Gur, Ruben C.; Kohler, Christian; Verma, Ragini

2008-01-01

Deficits in emotional expression are prominent in several neuropsychiatric disorders, including schizophrenia. Available clinical facial expression evaluations provide subjective and qualitative measurements, which are based on static 2D images that do not capture the temporal dynamics and subtleties of expression changes. Therefore, there is a need for automated, objective and quantitative measurements of facial expressions captured using videos. This paper presents a computational framework that creates probabilistic expression profiles for video data and can potentially help to automatically quantify emotional expression differences between patients with neuropsychiatric disorders and healthy controls. Our method automatically detects and tracks facial landmarks in videos, and then extracts geometric features to characterize facial expression changes. To analyze temporal facial expression changes, we employ probabilistic classifiers that analyze facial expressions in individual frames, and then propagate the probabilities throughout the video to capture the temporal characteristics of facial expressions. The applications of our method to healthy controls and case studies of patients with schizophrenia and Asperger’s syndrome demonstrate the capability of the video-based expression analysis method in capturing subtleties of facial expression. Such results can pave the way for a video based method for quantitative analysis of facial expressions in clinical research of disorders that cause affective deficits. PMID:18045693

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor

PubMed Central

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

2017-01-01

Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection. PMID:29292716

Audio feature extraction using probability distribution function

NASA Astrophysics Data System (ADS)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled persons.

PubMed

Nagarajan, R; Hariharan, M; Satiyan, M

2012-08-01

Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.

Recursive Feature Extraction in Graphs

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

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

Facial approximation-from facial reconstruction synonym to face prediction paradigm.

PubMed

Stephan, Carl N

2015-05-01

Facial approximation was first proposed as a synonym for facial reconstruction in 1987 due to dissatisfaction with the connotations the latter label held. Since its debut, facial approximation's identity has morphed as anomalies in face prediction have accumulated. Now underpinned by differences in what problems are thought to count as legitimate, facial approximation can no longer be considered a synonym for, or subclass of, facial reconstruction. Instead, two competing paradigms of face prediction have emerged, namely: facial approximation and facial reconstruction. This paper shines a Kuhnian lens across the discipline of face prediction to comprehensively review these developments and outlines the distinguishing features between the two paradigms. © 2015 American Academy of Forensic Sciences.

Face in profile view reduces perceived facial expression intensity: an eye-tracking study.

PubMed

Guo, Kun; Shaw, Heather

2015-02-01

Recent studies measuring the facial expressions of emotion have focused primarily on the perception of frontal face images. As we frequently encounter expressive faces from different viewing angles, having a mechanism which allows invariant expression perception would be advantageous to our social interactions. Although a couple of studies have indicated comparable expression categorization accuracy across viewpoints, it is unknown how perceived expression intensity and associated gaze behaviour change across viewing angles. Differences could arise because diagnostic cues from local facial features for decoding expressions could vary with viewpoints. Here we manipulated orientation of faces (frontal, mid-profile, and profile view) displaying six common facial expressions of emotion, and measured participants' expression categorization accuracy, perceived expression intensity and associated gaze patterns. In comparison with frontal faces, profile faces slightly reduced identification rates for disgust and sad expressions, but significantly decreased perceived intensity for all tested expressions. Although quantitatively viewpoint had expression-specific influence on the proportion of fixations directed at local facial features, the qualitative gaze distribution within facial features (e.g., the eyes tended to attract the highest proportion of fixations, followed by the nose and then the mouth region) was independent of viewpoint and expression type. Our results suggest that the viewpoint-invariant facial expression processing is categorical perception, which could be linked to a viewpoint-invariant holistic gaze strategy for extracting expressive facial cues. Copyright © 2014 Elsevier B.V. All rights reserved.

[Facial palsy].

PubMed

Cavoy, R

2013-09-01

Facial palsy is a daily challenge for the clinicians. Determining whether facial nerve palsy is peripheral or central is a key step in the diagnosis. Central nervous lesions can give facial palsy which may be easily differentiated from peripheral palsy. The next question is the peripheral facial paralysis idiopathic or symptomatic. A good knowledge of anatomy of facial nerve is helpful. A structure approach is given to identify additional features that distinguish symptomatic facial palsy from idiopathic one. The main cause of peripheral facial palsies is idiopathic one, or Bell's palsy, which remains a diagnosis of exclusion. The most common cause of symptomatic peripheral facial palsy is Ramsay-Hunt syndrome. Early identification of symptomatic facial palsy is important because of often worst outcome and different management. The prognosis of Bell's palsy is on the whole favorable and is improved with a prompt tapering course of prednisone. In Ramsay-Hunt syndrome, an antiviral therapy is added along with prednisone. We also discussed of current treatment recommendations. We will review short and long term complications of peripheral facial palsy.

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Eyeglasses Lens Contour Extraction from Facial Images Using an Efficient Shape Description

PubMed Central

Borza, Diana; Darabant, Adrian Sergiu; Danescu, Radu

2013-01-01

This paper presents a system that automatically extracts the position of the eyeglasses and the accurate shape and size of the frame lenses in facial images. The novelty brought by this paper consists in three key contributions. The first one is an original model for representing the shape of the eyeglasses lens, using Fourier descriptors. The second one is a method for generating the search space starting from a finite, relatively small number of representative lens shapes based on Fourier morphing. Finally, we propose an accurate lens contour extraction algorithm using a multi-stage Monte Carlo sampling technique. Multiple experiments demonstrate the effectiveness of our approach. PMID:24152926

Influence of skin ageing features on Chinese women's perception of facial age and attractiveness.

PubMed

Porcheron, A; Latreille, J; Jdid, R; Tschachler, E; Morizot, F

2014-08-01

Ageing leads to characteristic changes in the appearance of facial skin. Among these changes, we can distinguish the skin topographic cues (skin sagging and wrinkles), the dark spots and the dark circles around the eyes. Although skin changes are similar in Caucasian and Chinese faces, the age of occurrence and the severity of age-related features differ between the two populations. Little is known about how the ageing of skin influences the perception of female faces in Chinese women. The aim of this study is to evaluate the contribution of the different age-related skin features to the perception of age and attractiveness in Chinese women. Facial images of Caucasian women and Chinese women in their 60s were manipulated separately to reduce the following skin features: (i) skin sagging and wrinkles, (ii) dark spots and (iii) dark circles. Finally, all signs were reduced simultaneously (iv). Female Chinese participants were asked to estimate the age difference between the modified and original images and evaluate the attractiveness of modified and original faces. Chinese women perceived the Chinese faces as younger after the manipulation of dark spots than after the reduction in wrinkles/sagging, whereas they perceived the Caucasian faces as the youngest after the manipulation of wrinkles/sagging. Interestingly, Chinese women evaluated faces with reduced dark spots as being the most attractive whatever the origin of the face. The manipulation of dark circles contributed to making Caucasian and Chinese faces being perceived younger and more attractive than the original faces, although the effect was less pronounced than for the two other types of manipulation. This is the first study to have examined the influence of various age-related skin features on the facial age and attractiveness perception of Chinese women. The results highlight different contributions of dark spots, sagging/wrinkles and dark circles to their perception of Chinese and Caucasian faces. �

The relative importance of external and internal features of facial composites.

PubMed

Frowd, Charlie; Bruce, Vicki; McIntyre, Alex; Hancock, Peter

2007-02-01

Three experiments are reported that compare the quality of external with internal regions within a set of facial composites using two matching-type tasks. Composites are constructed with the aim of triggering recognition from people familiar with the targets, and past research suggests internal face features dominate representations of familiar faces in memory. However the experiments reported here show that the internal regions of composites are very poorly matched against the faces they purport to represent, while external feature regions alone were matched almost as well as complete composites. In Experiments 1 and 2 the composites used were constructed by participant-witnesses who were unfamiliar with the targets and therefore were predicted to demonstrate a bias towards the external parts of a face. In Experiment 3 we compared witnesses who were familiar or unfamiliar with the target items, but for both groups the external features were much better reproduced in the composites, suggesting it is the process of composite construction itself which is responsible for the poverty of the internal features. Practical implications of these results are discussed.

Comparative analysis of feature extraction methods in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Asif, Muhammad Rizwan; Ali, Saad

2017-10-01

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

Sensorineural Deafness, Distinctive Facial Features and Abnormal Cranial Bones

PubMed Central

Gad, Alona; Laurino, Mercy; Maravilla, Kenneth R.; Matsushita, Mark; Raskind, Wendy H.

2008-01-01

The Waardenburg syndromes (WS) account for approximately 2% of congenital sensorineural deafness. This heterogeneous group of diseases currently can be categorized into four major subtypes (WS types 1-4) on the basis of characteristic clinical features. Multiple genes have been implicated in WS, and mutations in some genes can cause more than one WS subtype. In addition to eye, hair and skin pigmentary abnormalities, dystopia canthorum and broad nasal bridge are seen in WS type 1. Mutations in the PAX3 gene are responsible for the condition in the majority of these patients. In addition, mutations in PAX3 have been found in WS type 3 that is distinguished by musculoskeletal abnormalities, and in a family with a rare subtype of WS, craniofacial-deafness-hand syndrome (CDHS), characterized by dysmorphic facial features, hand abnormalities, and absent or hypoplastic nasal and wrist bones. Here we describe a woman who shares some, but not all features of WS type 3 and CDHS, and who also has abnormal cranial bones. All sinuses were hypoplastic, and the cochlea were small. No sequence alteration in PAX3 was found. These observations broaden the clinical range of WS and suggest there may be genetic heterogeneity even within the CDHS subtype. PMID:18553554

Feature extraction for document text using Latent Dirichlet Allocation

NASA Astrophysics Data System (ADS)

Prihatini, P. M.; Suryawan, I. K.; Mandia, IN

2018-01-01

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.

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

A Diagnosis to Consider in an Adult Patient with Facial Features and Intellectual Disability: Williams Syndrome.

PubMed

Doğan, Özlem Akgün; Şimşek Kiper, Pelin Özlem; Utine, Gülen Eda; Alikaşifoğlu, Mehmet; Boduroğlu, Koray

2017-03-01

Williams syndrome (OMIM #194050) is a rare, well-recognized, multisystemic genetic condition affecting approximately 1/7,500 individuals. There are no marked regional differences in the incidence of Williams syndrome. The syndrome is caused by a hemizygous deletion of approximately 28 genes, including ELN on chromosome 7q11.2. Prenatal-onset growth retardation, distinct facial appearance, cardiovascular abnormalities, and unique hypersocial behavior are among the most common clinical features. Here, we report the case of a patient referred to us with distinct facial features and intellectual disability, who was diagnosed with Williams syndrome at the age of 37 years. Our aim is to increase awareness regarding the diagnostic features and complications of this recognizable syndrome among adult health care providers. Williams syndrome is usually diagnosed during infancy or childhood, but in the absence of classical findings, such as cardiovascular anomalies, hypercalcemia, and cognitive impairment, the diagnosis could be delayed. Due to the multisystemic and progressive nature of the syndrome, accurate diagnosis is critical for appropriate care and screening for the associated morbidities that may affect the patient's health and well-being.

Development and characterization of clay facial mask containing turmeric extract solid dispersion.

PubMed

Pan-On, Suchiwa; Rujivipat, Soravoot; Ounaroon, Anan; Tiyaboonchai, Waree

2018-04-01

To develop clay facial mask containing turmeric extract solid dispersion (TESD) for enhancing curcumin water solubility and permeability and to determine suitable clay based facial mask. The TESD were prepared by solvent and melting solvent method with various TE to polyvinylpyrrolidone (PVP) K30 mass ratios. The physicochemical properties, water solubility, and permeability were examined. The effects of clay types on physical stability of TESD, water adsorption, and curcumin adsorption capacity were evaluated. The TESD prepared by solvent method with a TE to PVP K30 mass ratio of 1:2 showed physically stable, dry powders, when mixed with clay. When TESD was dissolved in water, the obtained TESD micelles showed spherical shape with mean size of ∼100 nm resulting in a substantial enhancement of curcumin water solubility, ∼5 mg/ml. Bentonite (Bent) and mica (M) showed the highest water adsorption capacity. The TESD's color was altered when mixed with Bent, titanium dioxide (TiO 2 ) and zinc oxide (ZnO) indicating curcumin instability. Talcum (Talc) showed the greatest curcumin adsorption followed by M and kaolin (K), respectively. Consequently, in vitro permeation studies of the TESD mixed with Talc showed lowest curcumin permeation, while TESD mixed with M or K showed similar permeation profile as free TESD solutions. The developed TESD-based clay facial mask showed lower curcumin permeation as compared to those formulations with Tween 80. The water solubility and permeability of curcumin in clay based facial mask could be improved using solid dispersion technique and suitable clay base composed of K, M, and Talc.

An adaptation study of internal and external features in facial representations.

PubMed

Hills, Charlotte; Romano, Kali; Davies-Thompson, Jodie; Barton, Jason J S

2014-07-01

Prior work suggests that internal features contribute more than external features to face processing. Whether this asymmetry is also true of the mental representations of faces is not known. We used face adaptation to determine whether the internal and external features of faces contribute differently to the representation of facial identity, whether this was affected by familiarity, and whether the results differed if the features were presented in isolation or as part of a whole face. In a first experiment, subjects performed a study of identity adaptation for famous and novel faces, in which the adapting stimuli were whole faces, the internal features alone, or the external features alone. In a second experiment, the same faces were used, but the adapting internal and external features were superimposed on whole faces that were ambiguous to identity. The first experiment showed larger aftereffects for unfamiliar faces, and greater aftereffects from internal than from external features, and the latter was true for both familiar and unfamiliar faces. When internal and external features were presented in a whole-face context in the second experiment, aftereffects from either internal or external features was less than that from the whole face, and did not differ from each other. While we reproduce the greater importance of internal features when presented in isolation, we find this is equally true for familiar and unfamiliar faces. The dominant influence of internal features is reduced when integrated into a whole-face context, suggesting another facet of expert face processing. Copyright © 2014 Elsevier B.V. All rights reserved.

Facial Nerve Schwannoma: A Case Report, Radiological Features and Literature Review.

PubMed

Pilloni, Giulia; Mico, Barbara Massa; Altieri, Roberto; Zenga, Francesco; Ducati, Alessandro; Garbossa, Diego; Tartara, Fulvio

2017-12-22

Facial nerve schwannoma localized in the middle fossa is a rare lesion. We report a case of a facial nerve schwannoma in a 30-year-old male presenting with facial nerve palsy. Magnetic resonance imaging (MRI) showed a 3 cm diameter tumor of the right middle fossa. The tumor was removed using a sub-temporal approach. Intraoperative monitoring allowed for identification of the facial nerve, so it was not damaged during the surgical excision. Neurological clinical examination at discharge demonstrated moderate facial nerve improvement (Grade III House-Brackmann).

The face is not an empty canvas: how facial expressions interact with facial appearance.

PubMed

Hess, Ursula; Adams, Reginald B; Kleck, Robert E

2009-12-12

Faces are not simply blank canvases upon which facial expressions write their emotional messages. In fact, facial appearance and facial movement are both important social signalling systems in their own right. We here provide multiple lines of evidence for the notion that the social signals derived from facial appearance on the one hand and facial movement on the other interact in a complex manner, sometimes reinforcing and sometimes contradicting one another. Faces provide information on who a person is. Sex, age, ethnicity, personality and other characteristics that can define a person and the social group the person belongs to can all be derived from the face alone. The present article argues that faces interact with the perception of emotion expressions because this information informs a decoder's expectations regarding an expresser's probable emotional reactions. Facial appearance also interacts more directly with the interpretation of facial movement because some of the features that are used to derive personality or sex information are also features that closely resemble certain emotional expressions, thereby enhancing or diluting the perceived strength of particular expressions.

Enhanced facial recognition for thermal imagery using polarimetric imaging.

PubMed

Gurton, Kristan P; Yuffa, Alex J; Videen, Gorden W

2014-07-01

We present a series of long-wave-infrared (LWIR) polarimetric-based thermal images of facial profiles in which polarization-state information of the image-forming radiance is retained and displayed. The resultant polarimetric images show enhanced facial features, additional texture, and details that are not present in corresponding conventional thermal imagery. It has been generally thought that conventional thermal imagery (MidIR or LWIR) could not produce the detailed spatial information required for reliable human identification due to the so-called "ghosting" effect often seen in thermal imagery of human subjects. By using polarimetric information, we are able to extract subtle surface features of the human face, thus improving subject identification. Polarimetric image sets considered include the conventional thermal intensity image, S0, the two Stokes images, S1 and S2, and a Stokes image product called the degree-of-linear-polarization image.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Extraction of linear features on SAR imagery

NASA Astrophysics Data System (ADS)

Liu, Junyi; Li, Deren; Mei, Xin

2006-10-01

Linear features are usually extracted from SAR imagery by a few edge detectors derived from the contrast ratio edge detector with a constant probability of false alarm. On the other hand, the Hough Transform is an elegant way of extracting global features like curve segments from binary edge images. Randomized Hough Transform can reduce the computation time and memory usage of the HT drastically. While Randomized Hough Transform will bring about a great deal of cells invalid during the randomized sample. In this paper, we propose a new approach to extract linear features on SAR imagery, which is an almost automatic algorithm based on edge detection and Randomized Hough Transform. The presented improved method makes full use of the directional information of each edge candidate points so as to solve invalid cumulate problems. Applied result is in good agreement with the theoretical study, and the main linear features on SAR imagery have been extracted automatically. The method saves storage space and computational time, which shows its effectiveness and applicability.

Fixation to features and neural processing of facial expressions in a gender discrimination task

PubMed Central

Neath, Karly N.; Itier, Roxane J.

2017-01-01

Early face encoding, as reflected by the N170 ERP component, is sensitive to fixation to the eyes. Whether this sensitivity varies with facial expressions of emotion and can also be seen on other ERP components such as P1 and EPN, was investigated. Using eye-tracking to manipulate fixation on facial features, we found the N170 to be the only eye-sensitive component and this was true for fearful, happy and neutral faces. A different effect of fixation to features was seen for the earlier P1 that likely reflected general sensitivity to face position. An early effect of emotion (~120 ms) for happy faces was seen at occipital sites and was sustained until ~350 ms post-stimulus. For fearful faces, an early effect was seen around 80 ms followed by a later effect appearing at ~150 ms until ~300 ms at lateral posterior sites. Results suggests that in this emotion-irrelevant gender discrimination task, processing of fearful and happy expressions occurred early and largely independently of the eye-sensitivity indexed by the N170. Processing of the two emotions involved different underlying brain networks active at different times. PMID:26277653

Brief Report: Infants Developing with ASD Show a Unique Developmental Pattern of Facial Feature Scanning

ERIC Educational Resources Information Center

Rutherford, M. D.; Walsh, Jennifer A.; Lee, Vivian

2015-01-01

Infants are interested in eyes, but look preferentially at mouths toward the end of the first year, when word learning begins. Language delays are characteristic of children developing with autism spectrum disorder (ASD). We measured how infants at risk for ASD, control infants, and infants who later reached ASD criterion scanned facial features.…

Automatic Feature Extraction from Planetary Images

NASA Technical Reports Server (NTRS)

Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

2010-01-01

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Learning the spherical harmonic features for 3-D face recognition.

PubMed

Liu, Peijiang; Wang, Yunhong; Huang, Di; Zhang, Zhaoxiang; Chen, Liming

2013-03-01

In this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3-D facial surface. This is in clear contrast to most 3-D FR techniques which are either holistic or feature based, using local features extracted from distinctive points. First, 3-D face models are represented in a canonical representation, namely, spherical depth map, by which SHF can be calculated. Then, considering the predictive contribution of each SHF feature, especially in the presence of facial expression and occlusion, feature selection methods are used to improve the predictive performance and provide faster and more cost-effective predictors. Experiments have been carried out on three public 3-D face datasets, SHREC2007, FRGC v2.0, and Bosphorus, with increasing difficulties in terms of facial expression, pose, and occlusion, and which demonstrate the effectiveness of the proposed method.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos.

PubMed

Demirkus, Meltem; Precup, Doina; Clark, James J; Arbel, Tal

2016-06-01

Recent literature shows that facial attributes, i.e., contextual facial information, can be beneficial for improving the performance of real-world applications, such as face verification, face recognition, and image search. Examples of face attributes include gender, skin color, facial hair, etc. How to robustly obtain these facial attributes (traits) is still an open problem, especially in the presence of the challenges of real-world environments: non-uniform illumination conditions, arbitrary occlusions, motion blur and background clutter. What makes this problem even more difficult is the enormous variability presented by the same subject, due to arbitrary face scales, head poses, and facial expressions. In this paper, we focus on the problem of facial trait classification in real-world face videos. We have developed a fully automatic hierarchical and probabilistic framework that models the collective set of frame class distributions and feature spatial information over a video sequence. The experiments are conducted on a large real-world face video database that we have collected, labelled and made publicly available. The proposed method is flexible enough to be applied to any facial classification problem. Experiments on a large, real-world video database McGillFaces [1] of 18,000 video frames reveal that the proposed framework outperforms alternative approaches, by up to 16.96 and 10.13%, for the facial attributes of gender and facial hair, respectively.

Modeling 3D Facial Shape from DNA

PubMed Central

Claes, Peter; Liberton, Denise K.; Daniels, Katleen; Rosana, Kerri Matthes; Quillen, Ellen E.; Pearson, Laurel N.; McEvoy, Brian; Bauchet, Marc; Zaidi, Arslan A.; Yao, Wei; Tang, Hua; Barsh, Gregory S.; Absher, Devin M.; Puts, David A.; Rocha, Jorge; Beleza, Sandra; Pereira, Rinaldo W.; Baynam, Gareth; Suetens, Paul; Vandermeulen, Dirk; Wagner, Jennifer K.; Boster, James S.; Shriver, Mark D.

2014-01-01

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. The facial effects of these variables are summarized as response-based imputed predictor (RIP) variables, which are validated using self-reported sex, genomic ancestry, and observer-based facial ratings (femininity and proportional ancestry) and judgments (sex and population group). By jointly modeling sex, genomic ancestry, and genotype, the independent effects of particular alleles on facial features can be uncovered. Results on a set of 20 genes showing significant effects on facial features provide support for this approach as a novel means to identify genes affecting normal-range facial features and for approximating the appearance of a face from genetic markers. PMID:24651127

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Chondromyxoid fibroma of the mastoid facial nerve canal mimicking a facial nerve schwannoma.

PubMed

Thompson, Andrew L; Bharatha, Aditya; Aviv, Richard I; Nedzelski, Julian; Chen, Joseph; Bilbao, Juan M; Wong, John; Saad, Reda; Symons, Sean P

2009-07-01

Chondromyxoid fibroma of the skull base is a rare entity. Involvement of the temporal bone is particularly rare. We present an unusual case of progressive facial nerve paralysis with imaging and clinical findings most suggestive of a facial nerve schwannoma. The lesion was tubular in appearance, expanded the mastoid facial nerve canal, protruded out of the stylomastoid foramen, and enhanced homogeneously. The only unusual imaging feature was minor calcification within the tumor. Surgery revealed an irregular, cystic lesion. Pathology diagnosed a chondromyxoid fibroma involving the mastoid portion of the facial nerve canal, destroying the facial nerve.

Assessment of the facial features and chin development of fetuses with use of serial three-dimensional sonography and the mandibular size monogram in a Chinese population.

PubMed

Tsai, Meng-Yin; Lan, Kuo-Chung; Ou, Chia-Yo; Chen, Jen-Huang; Chang, Shiuh-Young; Hsu, Te-Yao

2004-02-01

Our purpose was to evaluate whether the application of serial three-dimensional (3D) sonography and the mandibular size monogram can allow observation of dynamic changes in facial features, as well as chin development in utero. The mandibular size monogram has been established through a cross-sectional study involving 183 fetal images. The serial changes of facial features and chin development are assessed in a cohort study involving 40 patients. The monogram reveals that the Biparietal distance (BPD)/Mandibular body length (MBL) ratio is gradually decreased with the advance of gestational age. The cohort study conducted with serial 3D sonography shows the same tendency. Both the images and the results of paired-samples t test (P<.001) statistical analysis suggest that the fetuses develop wider chins and broader facial features in later weeks. The serial 3D sonography and mandibular size monogram display disproportionate growth of the fetal head and chin that leads to changes in facial features in late gestation. This fact must be considered when we evaluate fetuses at risk for development of micrognathia.

Automatic 2.5-D Facial Landmarking and Emotion Annotation for Social Interaction Assistance.

PubMed

Zhao, Xi; Zou, Jianhua; Li, Huibin; Dellandrea, Emmanuel; Kakadiaris, Ioannis A; Chen, Liming

2016-09-01

People with low vision, Alzheimer's disease, and autism spectrum disorder experience difficulties in perceiving or interpreting facial expression of emotion in their social lives. Though automatic facial expression recognition (FER) methods on 2-D videos have been extensively investigated, their performance was constrained by challenges in head pose and lighting conditions. The shape information in 3-D facial data can reduce or even overcome these challenges. However, high expenses of 3-D cameras prevent their widespread use. Fortunately, 2.5-D facial data from emerging portable RGB-D cameras provide a good balance for this dilemma. In this paper, we propose an automatic emotion annotation solution on 2.5-D facial data collected from RGB-D cameras. The solution consists of a facial landmarking method and a FER method. Specifically, we propose building a deformable partial face model and fit the model to a 2.5-D face for localizing facial landmarks automatically. In FER, a novel action unit (AU) space-based FER method has been proposed. Facial features are extracted using landmarks and further represented as coordinates in the AU space, which are classified into facial expressions. Evaluated on three publicly accessible facial databases, namely EURECOM, FRGC, and Bosphorus databases, the proposed facial landmarking and expression recognition methods have achieved satisfactory results. Possible real-world applications using our algorithms have also been discussed.

Dysmorphic Facial Features and Other Clinical Characteristics in Two Patients with PEX1 Gene Mutations

PubMed Central

Gunduz, Mehmet

2016-01-01

Peroxisomal disorders are a group of genetically heterogeneous metabolic diseases related to dysfunction of peroxisomes. Dysmorphic features, neurological abnormalities, and hepatic dysfunction can be presenting signs of peroxisomal disorders. Here we presented dysmorphic facial features and other clinical characteristics in two patients with PEX1 gene mutation. Follow-up periods were 3.5 years and 1 year in the patients. Case I was one-year-old girl that presented with neurodevelopmental delay, hepatomegaly, bilateral hearing loss, and visual problems. Ophthalmologic examination suggested septooptic dysplasia. Cranial magnetic resonance imaging (MRI) showed nonspecific gliosis at subcortical and periventricular deep white matter. Case II was 2.5-year-old girl referred for investigation of global developmental delay and elevated liver enzymes. Ophthalmologic examination findings were consistent with bilateral nystagmus and retinitis pigmentosa. Cranial MRI was normal. Dysmorphic facial features including broad nasal root, low set ears, downward slanting eyes, downward slanting eyebrows, and epichantal folds were common findings in two patients. Molecular genetic analysis indicated homozygous novel IVS1-2A>G mutation in Case I and homozygous p.G843D (c.2528G>A) mutation in Case II in the PEX1 gene. Clinical findings and developmental prognosis vary in PEX1 gene mutation. Kabuki-like phenotype associated with liver pathology may indicate Zellweger spectrum disorders (ZSD). PMID:27882258

Fixation to features and neural processing of facial expressions in a gender discrimination task.

PubMed

Neath, Karly N; Itier, Roxane J

2015-10-01

Early face encoding, as reflected by the N170 ERP component, is sensitive to fixation to the eyes. Whether this sensitivity varies with facial expressions of emotion and can also be seen on other ERP components such as P1 and EPN, was investigated. Using eye-tracking to manipulate fixation on facial features, we found the N170 to be the only eye-sensitive component and this was true for fearful, happy and neutral faces. A different effect of fixation to features was seen for the earlier P1 that likely reflected general sensitivity to face position. An early effect of emotion (∼120 ms) for happy faces was seen at occipital sites and was sustained until ∼350 ms post-stimulus. For fearful faces, an early effect was seen around 80 ms followed by a later effect appearing at ∼150 ms until ∼300 ms at lateral posterior sites. Results suggests that in this emotion-irrelevant gender discrimination task, processing of fearful and happy expressions occurred early and largely independently of the eye-sensitivity indexed by the N170. Processing of the two emotions involved different underlying brain networks active at different times. Copyright © 2015 Elsevier Inc. All rights reserved.

Linguistic feature analysis for protein interaction extraction

PubMed Central

2009-01-01

Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518

Line fitting based feature extraction for object recognition

NASA Astrophysics Data System (ADS)

Li, Bing

2014-06-01

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

Incongruence Between Observers’ and Observed Facial Muscle Activation Reduces Recognition of Emotional Facial Expressions From Video Stimuli

PubMed Central

Wingenbach, Tanja S. H.; Brosnan, Mark; Pfaltz, Monique C.; Plichta, Michael M.; Ashwin, Chris

2018-01-01

According to embodied cognition accounts, viewing others’ facial emotion can elicit the respective emotion representation in observers which entails simulations of sensory, motor, and contextual experiences. In line with that, published research found viewing others’ facial emotion to elicit automatic matched facial muscle activation, which was further found to facilitate emotion recognition. Perhaps making congruent facial muscle activity explicit produces an even greater recognition advantage. If there is conflicting sensory information, i.e., incongruent facial muscle activity, this might impede recognition. The effects of actively manipulating facial muscle activity on facial emotion recognition from videos were investigated across three experimental conditions: (a) explicit imitation of viewed facial emotional expressions (stimulus-congruent condition), (b) pen-holding with the lips (stimulus-incongruent condition), and (c) passive viewing (control condition). It was hypothesised that (1) experimental condition (a) and (b) result in greater facial muscle activity than (c), (2) experimental condition (a) increases emotion recognition accuracy from others’ faces compared to (c), (3) experimental condition (b) lowers recognition accuracy for expressions with a salient facial feature in the lower, but not the upper face area, compared to (c). Participants (42 males, 42 females) underwent a facial emotion recognition experiment (ADFES-BIV) while electromyography (EMG) was recorded from five facial muscle sites. The experimental conditions’ order was counter-balanced. Pen-holding caused stimulus-incongruent facial muscle activity for expressions with facial feature saliency in the lower face region, which reduced recognition of lower face region emotions. Explicit imitation caused stimulus-congruent facial muscle activity without modulating recognition. Methodological implications are discussed. PMID:29928240

Incongruence Between Observers' and Observed Facial Muscle Activation Reduces Recognition of Emotional Facial Expressions From Video Stimuli.

PubMed

Wingenbach, Tanja S H; Brosnan, Mark; Pfaltz, Monique C; Plichta, Michael M; Ashwin, Chris

2018-01-01

According to embodied cognition accounts, viewing others' facial emotion can elicit the respective emotion representation in observers which entails simulations of sensory, motor, and contextual experiences. In line with that, published research found viewing others' facial emotion to elicit automatic matched facial muscle activation, which was further found to facilitate emotion recognition. Perhaps making congruent facial muscle activity explicit produces an even greater recognition advantage. If there is conflicting sensory information, i.e., incongruent facial muscle activity, this might impede recognition. The effects of actively manipulating facial muscle activity on facial emotion recognition from videos were investigated across three experimental conditions: (a) explicit imitation of viewed facial emotional expressions (stimulus-congruent condition), (b) pen-holding with the lips (stimulus-incongruent condition), and (c) passive viewing (control condition). It was hypothesised that (1) experimental condition (a) and (b) result in greater facial muscle activity than (c), (2) experimental condition (a) increases emotion recognition accuracy from others' faces compared to (c), (3) experimental condition (b) lowers recognition accuracy for expressions with a salient facial feature in the lower, but not the upper face area, compared to (c). Participants (42 males, 42 females) underwent a facial emotion recognition experiment (ADFES-BIV) while electromyography (EMG) was recorded from five facial muscle sites. The experimental conditions' order was counter-balanced. Pen-holding caused stimulus-incongruent facial muscle activity for expressions with facial feature saliency in the lower face region, which reduced recognition of lower face region emotions. Explicit imitation caused stimulus-congruent facial muscle activity without modulating recognition. Methodological implications are discussed.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Maxillary arch width and buccal corridor changes with orthodontic treatment. Part 2: attractiveness of the frontal facial smile in extraction and nonextraction outcomes.

PubMed

Meyer, Anna H; Woods, Michael G; Manton, David J

2014-03-01

This study was designed to assess the influence that the buccal corridor might have on the frontal facial attractiveness of subjects who had received orthodontic treatment with or without 4 premolar extractions. Posttreatment full-face frontal smiling photographs of 30 premolar extraction and 27 nonextraction patients were evaluated by 20 orthodontists, 20 dentists, and 20 laypeople using a visual analog scale. The ratings were analyzed according to rater group, rater sex, and number of years in practice for orthodontists and dentists to search for any statistically significant differences in the ratings on the basis of treatment groups, subject sex, and buccal corridor widths and areas. Orthodontists and dentists gave higher mean overall frontal facial attractiveness scores than did laypeople. There were no significant differences in how men and women rated the study subjects. The number of years in practice did not affect how the orthodontists rated, but it did affect the ratings of the dentists. Female subjects were consistently rated as significantly more attractive than male subjects. There was no difference in ratings for the extraction and nonextraction subject groups. The buccal corridor widths and areas did not affect the frontal facial attractiveness ratings. If treatment has been carried out with thorough diagnosis and careful planning, neither the choice of extraction or nonextraction treatment, nor the resulting buccal corridor widths or areas appeared to affect the subjects' frontal facial attractiveness. Copyright © 2014 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

A judicious multiple hypothesis tracker with interacting feature extraction

NASA Astrophysics Data System (ADS)

McAnanama, James G.; Kirubarajan, T.

2009-05-01

The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.

Qualitative and Quantitative Analysis for Facial Complexion in Traditional Chinese Medicine

PubMed Central

Zhao, Changbo; Li, Guo-zheng; Li, Fufeng; Wang, Zhi; Liu, Chang

2014-01-01

Facial diagnosis is an important and very intuitive diagnostic method in Traditional Chinese Medicine (TCM). However, due to its qualitative and experience-based subjective property, traditional facial diagnosis has a certain limitation in clinical medicine. The computerized inspection method provides classification models to recognize facial complexion (including color and gloss). However, the previous works only study the classification problems of facial complexion, which is considered as qualitative analysis in our perspective. For quantitative analysis expectation, the severity or degree of facial complexion has not been reported yet. This paper aims to make both qualitative and quantitative analysis for facial complexion. We propose a novel feature representation of facial complexion from the whole face of patients. The features are established with four chromaticity bases splitting up by luminance distribution on CIELAB color space. Chromaticity bases are constructed from facial dominant color using two-level clustering; the optimal luminance distribution is simply implemented with experimental comparisons. The features are proved to be more distinctive than the previous facial complexion feature representation. Complexion recognition proceeds by training an SVM classifier with the optimal model parameters. In addition, further improved features are more developed by the weighted fusion of five local regions. Extensive experimental results show that the proposed features achieve highest facial color recognition performance with a total accuracy of 86.89%. And, furthermore, the proposed recognition framework could analyze both color and gloss degrees of facial complexion by learning a ranking function. PMID:24967342

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

Facial anatomy.

PubMed

Marur, Tania; Tuna, Yakup; Demirci, Selman

2014-01-01

Dermatologic problems of the face affect both function and aesthetics, which are based on complex anatomical features. Treating dermatologic problems while preserving the aesthetics and functions of the face requires knowledge of normal anatomy. When performing successfully invasive procedures of the face, it is essential to understand its underlying topographic anatomy. This chapter presents the anatomy of the facial musculature and neurovascular structures in a systematic way with some clinically important aspects. We describe the attachments of the mimetic and masticatory muscles and emphasize their functions and nerve supply. We highlight clinically relevant facial topographic anatomy by explaining the course and location of the sensory and motor nerves of the face and facial vasculature with their relations. Additionally, this chapter reviews the recent nomenclature of the branching pattern of the facial artery. © 2013 Elsevier Inc. All rights reserved.

Human facial neural activities and gesture recognition for machine-interfacing applications.

PubMed

Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

2011-01-01

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

Adaptation of facial synthesis to parameter analysis in MPEG-4 visual communication

NASA Astrophysics Data System (ADS)

Yu, Lu; Zhang, Jingyu; Liu, Yunhai

2000-12-01

In MPEG-4, Facial Definition Parameters (FDPs) and Facial Animation Parameters (FAPs) are defined to animate 1 a facial object. Most of the previous facial animation reconstruction systems were focused on synthesizing animation from manually or automatically generated FAPs but not the FAPs extracted from natural video scene. In this paper, an analysis-synthesis MPEG-4 visual communication system is established, in which facial animation is reconstructed from FAPs extracted from natural video scene.

Appearance-based human gesture recognition using multimodal features for human computer interaction

NASA Astrophysics Data System (ADS)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Tracking subtle stereotypes of children with trisomy 21: from facial-feature-based to implicit stereotyping.

PubMed

Enea-Drapeau, Claire; Carlier, Michèle; Huguet, Pascal

2012-01-01

Stigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome), the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping. The participants were 165 adults including 55 young adult students, 55 non-student adults, and 55 professional caregivers working with intellectually disabled persons. They were faced with implicit association tests (IAT), a well-known technique whereby response latency is used to capture the relative strength with which some groups of people--here photographed faces of typically developing children and children with T21--are automatically (without conscious awareness) associated with positive versus negative attributes in memory. Each participant also rated the same photographed faces (consciously accessible evaluations). We provide the first evidence that the positive bias typically found in explicit judgments of children with T21 is smaller for those whose facial features are highly characteristic of this disorder, compared to their counterparts with less distinctive features and to typically developing children. We also show that this bias can coexist with negative evaluations at the implicit level (with large effect sizes), even among professional caregivers. These findings support recent models of feature-based stereotyping, and more importantly show how crucial it is to go beyond explicit evaluations to estimate the true extent of stigmatization of intellectually disabled people.

Facial expression recognition based on weber local descriptor and sparse representation

NASA Astrophysics Data System (ADS)

Ouyang, Yan

2018-03-01

Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

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.

Why 8-Year-Olds Cannot Tell the Difference between Steve Martin and Paul Newman: Factors Contributing to the Slow Development of Sensitivity to the Spacing of Facial Features

ERIC Educational Resources Information Center

Mondloch, Catherine J.; Dobson, Kate S.; Parsons, Julie; Maurer, Daphne

2004-01-01

Children are nearly as sensitive as adults to some cues to facial identity (e.g., differences in the shape of internal features and the external contour), but children are much less sensitive to small differences in the spacing of facial features. To identify factors that contribute to this pattern, we compared 8-year-olds' sensitivity to spacing…

Combining facial dynamics with appearance for age estimation.

PubMed

Dibeklioglu, Hamdi; Alnajar, Fares; Ali Salah, Albert; Gevers, Theo

2015-06-01

Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person's smile. Our approach is tested on a large, gender-balanced database with 400 subjects, with an age range between 8 and 76. In addition, we introduce a new database on posed disgust expressions with 324 subjects in the same age range, and evaluate the reliability of the proposed approach when used with another expression. State-of-the-art appearance-based age estimation methods from the literature are implemented as baseline. We demonstrate that for each of these methods, the addition of the proposed dynamic features results in statistically significant improvement. We further propose a novel hierarchical age estimation architecture based on adaptive age grouping. We test our approach extensively, including an exploration of spontaneous versus posed smile dynamics, and gender-specific age estimation. We show that using spontaneity information reduces the mean absolute error by up to 21%, advancing the state of the art for facial age estimation.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

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

2013-01-01

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

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Automated feature extraction and classification from image sources

USGS Publications Warehouse

,

1995-01-01

The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

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.

Mapping the impairment in decoding static facial expressions of emotion in prosopagnosia.

PubMed

Fiset, Daniel; Blais, Caroline; Royer, Jessica; Richoz, Anne-Raphaëlle; Dugas, Gabrielle; Caldara, Roberto

2017-08-01

Acquired prosopagnosia is characterized by a deficit in face recognition due to diverse brain lesions, but interestingly most prosopagnosic patients suffering from posterior lesions use the mouth instead of the eyes for face identification. Whether this bias is present for the recognition of facial expressions of emotion has not yet been addressed. We tested PS, a pure case of acquired prosopagnosia with bilateral occipitotemporal lesions anatomically sparing the regions dedicated for facial expression recognition. PS used mostly the mouth to recognize facial expressions even when the eye area was the most diagnostic. Moreover, PS directed most of her fixations towards the mouth. Her impairment was still largely present when she was instructed to look at the eyes, or when she was forced to look at them. Control participants showed a performance comparable to PS when only the lower part of the face was available. These observations suggest that the deficits observed in PS with static images are not solely attentional, but are rooted at the level of facial information use. This study corroborates neuroimaging findings suggesting that the Occipital Face Area might play a critical role in extracting facial features that are integrated for both face identification and facial expression recognition in static images. © The Author (2017). Published by Oxford University Press.

Association of Frontal and Lateral Facial Attractiveness.

PubMed

Gu, Jeffrey T; Avilla, David; Devcic, Zlatko; Karimi, Koohyar; Wong, Brian J F

2018-01-01

Despite the large number of studies focused on defining frontal or lateral facial attractiveness, no reports have examined whether a significant association between frontal and lateral facial attractiveness exists. To examine the association between frontal and lateral facial attractiveness and to identify anatomical features that may influence discordance between frontal and lateral facial beauty. Paired frontal and lateral facial synthetic images of 240 white women (age range, 18-25 years) were evaluated from September 30, 2004, to September 29, 2008, using an internet-based focus group (n = 600) on an attractiveness Likert scale of 1 to 10, with 1 being least attractive and 10 being most attractive. Data analysis was performed from December 6, 2016, to March 30, 2017. The association between frontal and lateral attractiveness scores was determined using linear regression. Outliers were defined as data outside the 95% individual prediction interval. To identify features that contribute to score discordance between frontal and lateral attractiveness scores, each of these image pairs were scrutinized by an evaluator panel for facial features that were present in the frontal or lateral projections and absent in the other respective facial projections. Attractiveness scores obtained from internet-based focus groups. For the 240 white women studied (mean [SD] age, 21.4 [2.2] years), attractiveness scores ranged from 3.4 to 9.5 for frontal images and 3.3 to 9.4 for lateral images. The mean (SD) frontal attractiveness score was 6.9 (1.4), whereas the mean (SD) lateral attractiveness score was 6.4 (1.3). Simple linear regression of frontal and lateral attractiveness scores resulted in a coefficient of determination of r2 = 0.749. Eight outlier pairs were identified and analyzed by panel evaluation. Panel evaluation revealed no clinically applicable association between frontal and lateral images among outliers; however, contributory facial features were suggested

Enhancement of human skin facial revitalization by moringa leaf extract cream.

PubMed

Ali, Atif; Akhtar, Naveed; Chowdhary, Farzana

2014-05-01

Solar ultraviolet exposure is the main cause of skin damage by initiation of reactive oxygen species (ROS) leading to skin collagen imperfection and eventually skin roughness. This can be reduced by proper revitalization of skin enhancing younger and healthier appearance. To evaluate the skin facial revitalization effect of a cream formulation containing the Moringa oleifera leaf extract on humans. Active cream containing 3% of the concentrated extract of moringa leaves was developed by entrapping in the inner aqueous phase of cream. Base contained no extract. Skin revitalizing parameters, i.e. surface, volume, texture parameters and surface evaluation of the living skin (SELS) were assessed comparatively after application of the base and active cream on human face using Visioscan(®) VC 98 for a period of 3 months. Surface values were increased by the base and decreased by the active cream. Effects produced for the base and active cream were significant and insignificant, respectively, as observed in the case of surface. Unlike the base, the active cream showed significant effects on skin volume, texture parameters (energy, variance and contrast) and SELS, SEr (skin roughness), SEsc (skin scaliness), SEsm (skin smoothness), and SEw (skin wrinkles) parameters. The results suggested that moringa cream enhances skin revitalization effect and supports anti-aging skin effects.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Influence of gravity upon some facial signs.

PubMed

Flament, F; Bazin, R; Piot, B

2015-06-01

Facial clinical signs and their integration are the basis of perception than others could have from ourselves, noticeably the age they imagine we are. Facial modifications in motion and their objective measurements before and after application of skin regimen are essential to go further in evaluation capacities to describe efficacy in facial dynamics. Quantification of facial modifications vis à vis gravity will allow us to answer about 'control' of facial shape in daily activities. Standardized photographs of the faces of 30 Caucasian female subjects of various ages (24-73 year) were successively taken at upright and supine positions within a short time interval. All these pictures were therefore reframed - any bias due to facial features was avoided when evaluating one single sign - for clinical quotation by trained experts of several facial signs regarding published standardized photographic scales. For all subjects, the supine position increased facial width but not height, giving a more fuller appearance to the face. More importantly, the supine position changed the severity of facial ageing features (e.g. wrinkles) compared to an upright position and whether these features were attenuated or exacerbated depended on their facial location. Supine station mostly modifies signs of the lower half of the face whereas those of the upper half appear unchanged or slightly accentuated. These changes appear much more marked in the older groups, where some deep labial folds almost vanish. These alterations decreased the perceived ages of the subjects by an average of 3.8 years. Although preliminary, this study suggests that a 90° rotation of the facial skin vis à vis gravity induces rapid rearrangements among which changes in tensional forces within and across the face, motility of interstitial free water among underlying skin tissue and/or alterations of facial Langer lines, likely play a significant role. © 2015 Society of Cosmetic Scientists and the Société Fran

Tracking Subtle Stereotypes of Children with Trisomy 21: From Facial-Feature-Based to Implicit Stereotyping

PubMed Central

Enea-Drapeau, Claire; Carlier, Michèle; Huguet, Pascal

2012-01-01

Background Stigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome), the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping. Methodology/Principal Findings The participants were 165 adults including 55 young adult students, 55 non-student adults, and 55 professional caregivers working with intellectually disabled persons. They were faced with implicit association tests (IAT), a well-known technique whereby response latency is used to capture the relative strength with which some groups of people—here photographed faces of typically developing children and children with T21—are automatically (without conscious awareness) associated with positive versus negative attributes in memory. Each participant also rated the same photographed faces (consciously accessible evaluations). We provide the first evidence that the positive bias typically found in explicit judgments of children with T21 is smaller for those whose facial features are highly characteristic of this disorder, compared to their counterparts with less distinctive features and to typically developing children. We also show that this bias can coexist with negative evaluations at the implicit level (with large effect sizes), even among professional caregivers. Conclusion These findings support recent models of feature-based stereotyping, and more importantly show how crucial it is to go beyond explicit evaluations to estimate the true extent of stigmatization of intellectually disabled people. PMID:22496796

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Allergenic Ingredients in Facial Wet Wipes.

PubMed

Aschenbeck, Kelly A; Warshaw, Erin M

Allergic contact dermatitis commonly occurs on the face. Facial cleansing wipes may be an underrecognized source of allergens. The aim of this study was to determine the frequency of potentially allergenic ingredients in facial wet wipes. Ingredient lists from name brand and generic facial wipes from 4 large retailers were analyzed. In the 178 facial wipes examined, a total of 485 ingredients were identified (average, 16.7 ingredients per wipe). Excluding botanicals, the top 15 potentially allergenic ingredients were glycerin (64.0%), fragrance (63.5%), phenoxyethanol (53.9%), citric acid (51.1%), disodium EDTA (44.4%), sorbic acid derivatives (39.3%), tocopherol derivatives (38.8%), polyethylene glycol derivatives (32.6%), glyceryl stearate (31.5%), sodium citrate (29.8%), glucosides (27.5%), cetearyl alcohol (25.8%), propylene glycol (25.3%), sodium benzoate (24.2%), and ceteareth-20 (23.6%)/parabens (23.6%). Of note, methylisothiazolinone (2.2%) and methylchloroisothiazolinone (1.1%) were uncommon. The top potential allergens of botanical origin included Aloe barbadensis (41.0%), chamomile extracts (27.0%), tea extracts (21.3%), Cucumis sativus (20.2%), and Hamamelis virginiana (10.7%). Many potential allergens are present in facial wet wipes, including fragrances, preservatives, botanicals, glucosides, and propylene glycol.

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

PubMed Central

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T.; Alcázar-Ramírez, José D.; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A.

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI. PMID:26664493

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.

PubMed

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T; Alcázar-Ramírez, José D; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

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.

Modified kernel-based nonlinear feature extraction.

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

Ma, J.; Perkins, S. J.; Theiler, J. P.

2002-01-01

Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determinedmore » by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.« less

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Factors contributing to the adaptation aftereffects of facial expression.

PubMed

Butler, Andrea; Oruc, Ipek; Fox, Christopher J; Barton, Jason J S

2008-01-29

Previous studies have demonstrated the existence of adaptation aftereffects for facial expressions. Here we investigated which aspects of facial stimuli contribute to these aftereffects. In Experiment 1, we examined the role of local adaptation to image elements such as curvature, shape and orientation, independent of expression, by using hybrid faces constructed from either the same or opposing expressions. While hybrid faces made with consistent expressions generated aftereffects as large as those with normal faces, there were no aftereffects from hybrid faces made from different expressions, despite the fact that these contained the same local image elements. In Experiment 2, we examined the role of facial features independent of the normal face configuration by contrasting adaptation with whole faces to adaptation with scrambled faces. We found that scrambled faces also generated significant aftereffects, indicating that expressive features without a normal facial configuration could generate expression aftereffects. In Experiment 3, we examined the role of facial configuration by using schematic faces made from line elements that in isolation do not carry expression-related information (e.g. curved segments and straight lines) but that convey an expression when arranged in a normal facial configuration. We obtained a significant aftereffect for facial configurations but not scrambled configurations of these line elements. We conclude that facial expression aftereffects are not due to local adaptation to image elements but due to high-level adaptation of neural representations that involve both facial features and facial configuration.

Neural processing of fearful and happy facial expressions during emotion-relevant and emotion-irrelevant tasks: a fixation-to-feature approach

PubMed Central

Neath-Tavares, Karly N.; Itier, Roxane J.

2017-01-01

Research suggests an important role of the eyes and mouth for discriminating facial expressions of emotion. A gaze-contingent procedure was used to test the impact of fixation to facial features on the neural response to fearful, happy and neutral facial expressions in an emotion discrimination (Exp.1) and an oddball detection (Exp.2) task. The N170 was the only eye-sensitive ERP component, and this sensitivity did not vary across facial expressions. In both tasks, compared to neutral faces, responses to happy expressions were seen as early as 100–120ms occipitally, while responses to fearful expressions started around 150ms, on or after the N170, at both occipital and lateral-posterior sites. Analyses of scalp topographies revealed different distributions of these two emotion effects across most of the epoch. Emotion processing interacted with fixation location at different times between tasks. Results suggest a role of both the eyes and mouth in the neural processing of fearful expressions and of the mouth in the processing of happy expressions, before 350ms. PMID:27430934

Neural processing of fearful and happy facial expressions during emotion-relevant and emotion-irrelevant tasks: A fixation-to-feature approach.

PubMed

Neath-Tavares, Karly N; Itier, Roxane J

2016-09-01

Research suggests an important role of the eyes and mouth for discriminating facial expressions of emotion. A gaze-contingent procedure was used to test the impact of fixation to facial features on the neural response to fearful, happy and neutral facial expressions in an emotion discrimination (Exp.1) and an oddball detection (Exp.2) task. The N170 was the only eye-sensitive ERP component, and this sensitivity did not vary across facial expressions. In both tasks, compared to neutral faces, responses to happy expressions were seen as early as 100-120ms occipitally, while responses to fearful expressions started around 150ms, on or after the N170, at both occipital and lateral-posterior sites. Analyses of scalp topographies revealed different distributions of these two emotion effects across most of the epoch. Emotion processing interacted with fixation location at different times between tasks. Results suggest a role of both the eyes and mouth in the neural processing of fearful expressions and of the mouth in the processing of happy expressions, before 350ms. Copyright © 2016 Elsevier B.V. All rights reserved.

Perceptual and affective mechanisms in facial expression recognition: An integrative review.

PubMed

Calvo, Manuel G; Nummenmaa, Lauri

2016-09-01

Facial expressions of emotion involve a physical component of morphological changes in a face and an affective component conveying information about the expresser's internal feelings. It remains unresolved how much recognition and discrimination of expressions rely on the perception of morphological patterns or the processing of affective content. This review of research on the role of visual and emotional factors in expression recognition reached three major conclusions. First, behavioral, neurophysiological, and computational measures indicate that basic expressions are reliably recognized and discriminated from one another, albeit the effect may be inflated by the use of prototypical expression stimuli and forced-choice responses. Second, affective content along the dimensions of valence and arousal is extracted early from facial expressions, although this coarse affective representation contributes minimally to categorical recognition of specific expressions. Third, the physical configuration and visual saliency of facial features contribute significantly to expression recognition, with "emotionless" computational models being able to reproduce some of the basic phenomena demonstrated in human observers. We conclude that facial expression recognition, as it has been investigated in conventional laboratory tasks, depends to a greater extent on perceptual than affective information and mechanisms.

Facial emotion recognition and borderline personality pathology.

PubMed

Meehan, Kevin B; De Panfilis, Chiara; Cain, Nicole M; Antonucci, Camilla; Soliani, Antonio; Clarkin, John F; Sambataro, Fabio

2017-09-01

The impact of borderline personality pathology on facial emotion recognition has been in dispute; with impaired, comparable, and enhanced accuracy found in high borderline personality groups. Discrepancies are likely driven by variations in facial emotion recognition tasks across studies (stimuli type/intensity) and heterogeneity in borderline personality pathology. This study evaluates facial emotion recognition for neutral and negative emotions (fear/sadness/disgust/anger) presented at varying intensities. Effortful control was evaluated as a moderator of facial emotion recognition in borderline personality. Non-clinical multicultural undergraduates (n = 132) completed a morphed facial emotion recognition task of neutral and negative emotional expressions across different intensities (100% Neutral; 25%/50%/75% Emotion) and self-reported borderline personality features and effortful control. Greater borderline personality features related to decreased accuracy in detecting neutral faces, but increased accuracy in detecting negative emotion faces, particularly at low-intensity thresholds. This pattern was moderated by effortful control; for individuals with low but not high effortful control, greater borderline personality features related to misattributions of emotion to neutral expressions, and enhanced detection of low-intensity emotional expressions. Individuals with high borderline personality features may therefore exhibit a bias toward detecting negative emotions that are not or barely present; however, good self-regulatory skills may protect against this potential social-cognitive vulnerability. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Cosmetics as a feature of the extended human phenotype: modulation of the perception of biologically important facial signals.

PubMed

Etcoff, Nancy L; Stock, Shannon; Haley, Lauren E; Vickery, Sarah A; House, David M

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

PubMed Central

Etcoff, Nancy L.; Stock, Shannon; Haley, Lauren E.; Vickery, Sarah A.; House, David M.

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Texture Analysis and Cartographic Feature Extraction.

DTIC Science & Technology

1985-01-01

Investigations into using various image descriptors as well as developing interactive feature extraction software on the Digital Image Analysis Laboratory...system. Originator-supplied keywords: Ad-Hoc image descriptor; Bayes classifier; Bhattachryya distance; Clustering; Digital Image Analysis Laboratory

Automatic image assessment from facial attributes

NASA Astrophysics Data System (ADS)

Ptucha, Raymond; Kloosterman, David; Mittelstaedt, Brian; Loui, Alexander

2013-03-01

Personal consumer photography collections often contain photos captured by numerous devices stored both locally and via online services. The task of gathering, organizing, and assembling still and video assets in preparation for sharing with others can be quite challenging. Current commercial photobook applications are mostly manual-based requiring significant user interactions. To assist the consumer in organizing these assets, we propose an automatic method to assign a fitness score to each asset, whereby the top scoring assets are used for product creation. Our method uses cues extracted from analyzing pixel data, metadata embedded in the file, as well as ancillary tags or online comments. When a face occurs in an image, its features have a dominating influence on both aesthetic and compositional properties of the displayed image. As such, this paper will emphasize the contributions faces have on affecting the overall fitness score of an image. To understand consumer preference, we conducted a psychophysical study that spanned 27 judges, 5,598 faces, and 2,550 images. Preferences on a per-face and per-image basis were independently gathered to train our classifiers. We describe how to use machine learning techniques to merge differing facial attributes into a single classifier. Our novel methods of facial weighting, fusion of facial attributes, and dimensionality reduction produce stateof- the-art results suitable for commercial applications.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

The Influence of Changes in Size and Proportion of Selected Facial Features (Eyes, Nose, Mouth) on Assessment of Similarity between Female Faces.

PubMed

Lewandowski, Zdzisław

2015-09-01

The project aimed at finding the answers to the following two questions: to what extent does a change in size, height or width of the selected facial features influence the assessment of likeness between an original female composite portrait and a modified one? And how does the sex of the person who judges the images have an impact on the perception of likeness of facial features? The first stage of the project consisted of creating the image of the averaged female faces. Then the basic facial features like eyes, nose and mouth were cut out of the averaged face and each of these features was transformed in three ways: its size was changed by reduction or enlargement, its height was modified through reduction or enlargement of the above-mentioned features and its width was altered through widening or narrowing. In each out of six feature alternation methods, intensity of modification reached up to 20% of the original size with changes every 2%. The features altered in such a way were again stuck onto the original faces and retouched. The third stage consisted of the assessment, performed by the judges of both sexes, of the extent of likeness between the averaged composite portrait (without any changes) and the modified portraits. The results indicate that there are significant differences in the assessment of likeness of the portraits with some features modified to the original ones. The images with changes in the size and height of the nose received the lowest scores on the likeness scale, which indicates that these changes were perceived by the subjects as the most important. The photos with changes in the height of lip vermillion thickness (the lip height), lip width and the height and width of eye slit, in turn, received high scores of likeness, in spite of big changes, which signifies that these modifications were perceived as less important when compared to the other features investigated.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Information processing of motion in facial expression and the geometry of dynamical systems

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Eghbalnia, Hamid; McMenamin, Brenton W.

2005-01-01

An interesting problem in analysis of video data concerns design of algorithms that detect perceptually significant features in an unsupervised manner, for instance methods of machine learning for automatic classification of human expression. A geometric formulation of this genre of problems could be modeled with help of perceptual psychology. In this article, we outline one approach for a special case where video segments are to be classified according to expression of emotion or other similar facial motions. The encoding of realistic facial motions that convey expression of emotions for a particular person P forms a parameter space XP whose study reveals the "objective geometry" for the problem of unsupervised feature detection from video. The geometric features and discrete representation of the space XP are independent of subjective evaluations by observers. While the "subjective geometry" of XP varies from observer to observer, levels of sensitivity and variation in perception of facial expressions appear to share a certain level of universality among members of similar cultures. Therefore, statistical geometry of invariants of XP for a sample of population could provide effective algorithms for extraction of such features. In cases where frequency of events is sufficiently large in the sample data, a suitable framework could be provided to facilitate the information-theoretic organization and study of statistical invariants of such features. This article provides a general approach to encode motion in terms of a particular genre of dynamical systems and the geometry of their flow. An example is provided to illustrate the general theory.

Enhancement of human skin facial revitalization by moringa leaf extract cream

PubMed Central

Akhtar, Naveed; Chowdhary, Farzana

2014-01-01

Introduction Solar ultraviolet exposure is the main cause of skin damage by initiation of reactive oxygen species (ROS) leading to skin collagen imperfection and eventually skin roughness. This can be reduced by proper revitalization of skin enhancing younger and healthier appearance. Aim To evaluate the skin facial revitalization effect of a cream formulation containing the Moringa oleifera leaf extract on humans. Material and methods Active cream containing 3% of the concentrated extract of moringa leaves was developed by entrapping in the inner aqueous phase of cream. Base contained no extract. Skin revitalizing parameters, i.e. surface, volume, texture parameters and surface evaluation of the living skin (SELS) were assessed comparatively after application of the base and active cream on human face using Visioscan® VC 98 for a period of 3 months. Results Surface values were increased by the base and decreased by the active cream. Effects produced for the base and active cream were significant and insignificant, respectively, as observed in the case of surface. Unlike the base, the active cream showed significant effects on skin volume, texture parameters (energy, variance and contrast) and SELS, SEr (skin roughness), SEsc (skin scaliness), SEsm (skin smoothness), and SEw (skin wrinkles) parameters. Conclusions The results suggested that moringa cream enhances skin revitalization effect and supports anti-aging skin effects. PMID:25097471

Feature extraction from multiple data sources using genetic programming

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.

2002-08-01

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Local intensity area descriptor for facial recognition in ideal and noise conditions

NASA Astrophysics Data System (ADS)

Tran, Chi-Kien; Tseng, Chin-Dar; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Lee, Tsair-Fwu

2017-03-01

We propose a local texture descriptor, local intensity area descriptor (LIAD), which is applied for human facial recognition in ideal and noisy conditions. Each facial image is divided into small regions from which LIAD histograms are extracted and concatenated into a single feature vector to represent the facial image. The recognition is performed using a nearest neighbor classifier with histogram intersection and chi-square statistics as dissimilarity measures. Experiments were conducted with LIAD using the ORL database of faces (Olivetti Research Laboratory, Cambridge), the Face94 face database, the Georgia Tech face database, and the FERET database. The results demonstrated the improvement in accuracy of our proposed descriptor compared to conventional descriptors [local binary pattern (LBP), uniform LBP, local ternary pattern, histogram of oriented gradients, and local directional pattern]. Moreover, the proposed descriptor was less sensitive to noise and had low histogram dimensionality. Thus, it is expected to be a powerful texture descriptor that can be used for various computer vision problems.

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Changing facial phenotype in Cohen syndrome: towards clues for an earlier diagnosis.

PubMed

El Chehadeh-Djebbar, Salima; Blair, Edward; Holder-Espinasse, Muriel; Moncla, Anne; Frances, Anne-Marie; Rio, Marlène; Debray, François-Guillaume; Rump, Patrick; Masurel-Paulet, Alice; Gigot, Nadège; Callier, Patrick; Duplomb, Laurence; Aral, Bernard; Huet, Frédéric; Thauvin-Robinet, Christel; Faivre, Laurence

2013-07-01

Cohen syndrome (CS) is a rare autosomal recessive condition caused by mutations and/or large rearrangements in the VPS13B gene. CS clinical features, including developmental delay, the typical facial gestalt, chorioretinal dystrophy (CRD) and neutropenia, are well described. CS diagnosis is generally raised after school age, when visual disturbances lead to CRD diagnosis and to VPS13B gene testing. This relatively late diagnosis precludes accurate genetic counselling. The aim of this study was to analyse the evolution of CS facial features in the early period of life, particularly before school age (6 years), to find clues for an earlier diagnosis. Photographs of 17 patients with molecularly confirmed CS were analysed, from birth to preschool age. By comparing their facial phenotype when growing, we show that there are no special facial characteristics before 1 year. However, between 2 and 6 years, CS children already share common facial features such as a short neck, a square face with micrognathia and full cheeks, a hypotonic facial appearance, epicanthic folds, long ears with an everted upper part of the auricle and/or a prominent lobe, a relatively short philtrum, a small and open mouth with downturned corners, a thick lower lip and abnormal eye shapes. These early transient facial features evolve to typical CS facial features with aging. These observations emphasize the importance of ophthalmological tests and neutrophil count in children in preschool age presenting with developmental delay, hypotonia and the facial features we described here, for an earlier CS diagnosis.

Replicating distinctive facial features in lineups: identification performance in young versus older adults.

PubMed

Badham, Stephen P; Wade, Kimberley A; Watts, Hannah J E; Woods, Natalie G; Maylor, Elizabeth A

2013-04-01

Criminal suspects with distinctive facial features, such as tattoos or bruising, may stand out in a police lineup. To prevent suspects from being unfairly identified on the basis of their distinctive feature, the police often manipulate lineup images to ensure that all of the members appear similar. Recent research shows that replicating a distinctive feature across lineup members enhances eyewitness identification performance, relative to removing that feature on the target. In line with this finding, the present study demonstrated that with young adults (n = 60; mean age = 20), replication resulted in more target identifications than did removal in target-present lineups and that replication did not impair performance, relative to removal, in target-absent lineups. Older adults (n = 90; mean age = 74) performed significantly worse than young adults, identifying fewer targets and more foils; moreover, older adults showed a minimal benefit from replication over removal. This pattern is consistent with the associative deficit hypothesis of aging, such that older adults form weaker links between faces and their distinctive features. Although replication did not produce much benefit over removal for older adults, it was not detrimental to their performance. Therefore, the results suggest that replication may not be as beneficial to older adults as it is to young adults and demonstrate a new practical implication of age-related associative deficits in memory.

Automated Fluid Feature Extraction from Transient Simulations

NASA Technical Reports Server (NTRS)

Haimes, Robert; Lovely, David

1999-01-01

In the past, feature extraction and identification were interesting concepts, but not required to understand the underlying physics of a steady flow field. This is because the results of the more traditional tools like iso-surfaces, cuts and streamlines were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of much interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snap-shot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments like pV3). And methods must be developed to abstract the feature and display it in a manner that physically makes sense. The following is a list of the important physical phenomena found in transient (and steady-state) fluid flow: (1) Shocks, (2) Vortex cores, (3) Regions of recirculation, (4) Boundary layers, (5) Wakes. Three papers and an initial specification for the (The Fluid eXtraction tool kit) FX Programmer's guide were included. The papers, submitted to the AIAA Computational Fluid Dynamics Conference, are entitled : (1) Using Residence Time for the Extraction of Recirculation Regions, (2) Shock Detection from Computational Fluid Dynamics results and (3) On the Velocity Gradient Tensor and Fluid Feature Extraction.

Comparison of facial features of DiGeorge syndrome (DGS) due to deletion 10p13-10pter with DGS due to 22q11 deletion

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

Goodship, J.; Lynch, S.; Brown, J.

1994-09-01

DiGeorge syndrome (DGS) is a congenital anomaly consisting of cardiac defects, aplasia or hypoplasia of the thymus and parathroid glands, and dysmorphic facial features. The majority of DGS cases have a submicroscopic deletion within chromosome 22q11. However there have been a number of reports of DGS in association with other chromosomal abnormalities including four cases with chromosome 10p deletions. We describe a further 10p deletion case and suggest that the facial features in children with DGS due to deletions of 10p are different from those associated with chromosome 22 deletions. The propositus was born at 39 weeks gestation to unrelatedmore » caucasian parents, birth weight 2580g (10th centile) and was noted to be dysmorphic and cyanosed shortly after birth. The main dysmorphic facial features were a broad nasal bridge with very short palpebral fissures. Echocardiography revealed a large subsortic VSD and overriding aorta. She had a low ionised calcium and low parathroid hormone level. T cell subsets and PHA response were normal. Abdominal ultrasound showed duplex kidneys and on further investigation she was found to have reflux and raised plasma creatinine. She had an anteriorly placed anus. Her karyotype was 46,XX,-10,+der(10)t(3;10)(p23;p13)mat. The dysmorphic facial features in this baby are strikingly similar to those noted by Bridgeman and Butler in child with DGS as the result of a 10p deletion and distinct from the face seen in children with DiGeorge syndrome resulting from interstitial chromosome 22 deletions.« less

Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT)

NASA Astrophysics Data System (ADS)

Patil, Sandeep Baburao; Sinha, G. R.

2017-02-01

India, having less awareness towards the deaf and dumb peoples leads to increase the communication gap between deaf and hard hearing community. Sign language is commonly developed for deaf and hard hearing peoples to convey their message by generating the different sign pattern. The scale invariant feature transform was introduced by David Lowe to perform reliable matching between different images of the same object. This paper implements the various phases of scale invariant feature transform to extract the distinctive features from Indian sign language gestures. The experimental result shows the time constraint for each phase and the number of features extracted for 26 ISL gestures.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

Evaluation of facial expression in acute pain in cats.

PubMed

Holden, E; Calvo, G; Collins, M; Bell, A; Reid, J; Scott, E M; Nolan, A M

2014-12-01

To describe the development of a facial expression tool differentiating pain-free cats from those in acute pain. Observers shown facial images from painful and pain-free cats were asked to identify if they were in pain or not. From facial images, anatomical landmarks were identified and distances between these were mapped. Selected distances underwent statistical analysis to identify features discriminating pain-free and painful cats. Additionally, thumbnail photographs were reviewed by two experts to identify discriminating facial features between the groups. Observers (n = 68) had difficulty in identifying pain-free from painful cats, with only 13% of observers being able to discriminate more than 80% of painful cats. Analysis of 78 facial landmarks and 80 distances identified six significant factors differentiating pain-free and painful faces including ear position and areas around the mouth/muzzle. Standardised mouth and ear distances when combined showed excellent discrimination properties, correctly differentiating pain-free and painful cats in 98% of cases. Expert review supported these findings and a cartoon-type picture scale was developed from thumbnail images. Initial investigation into facial features of painful and pain-free cats suggests potentially good discrimination properties of facial images. Further testing is required for development of a clinical tool. © 2014 British Small Animal Veterinary Association.

Human Amygdala Tracks a Feature-Based Valence Signal Embedded within the Facial Expression of Surprise.

PubMed

Kim, M Justin; Mattek, Alison M; Bennett, Randi H; Solomon, Kimberly M; Shin, Jin; Whalen, Paul J

2017-09-27

Human amygdala function has been traditionally associated with processing the affective valence (negative vs positive) of an emotionally charged event, especially those that signal fear or threat. However, this account of human amygdala function can be explained by alternative views, which posit that the amygdala might be tuned to either (1) general emotional arousal (activation vs deactivation) or (2) specific emotion categories (fear vs happy). Delineating the pure effects of valence independent of arousal or emotion category is a challenging task, given that these variables naturally covary under many circumstances. To circumvent this issue and test the sensitivity of the human amygdala to valence values specifically, we measured the dimension of valence within the single facial expression category of surprise. Given the inherent valence ambiguity of this category, we show that surprised expression exemplars are attributed valence and arousal values that are uniquely and naturally uncorrelated. We then present fMRI data from both sexes, showing that the amygdala tracks these consensus valence values. Finally, we provide evidence that these valence values are linked to specific visual features of the mouth region, isolating the signal by which the amygdala detects this valence information. SIGNIFICANCE STATEMENT There is an open question as to whether human amygdala function tracks the valence value of cues in the environment, as opposed to either a more general emotional arousal value or a more specific emotion category distinction. Here, we demonstrate the utility of surprised facial expressions because exemplars within this emotion category take on valence values spanning the dimension of bipolar valence (positive to negative) at a consistent level of emotional arousal. Functional neuroimaging data showed that amygdala responses tracked the valence of surprised facial expressions, unconfounded by arousal. Furthermore, a machine learning classifier identified

Efficient feature extraction from wide-area motion imagery by MapReduce in Hadoop

NASA Astrophysics Data System (ADS)

Cheng, Erkang; Ma, Liya; Blaisse, Adam; Blasch, Erik; Sheaff, Carolyn; Chen, Genshe; Wu, Jie; Ling, Haibin

2014-06-01

Wide-Area Motion Imagery (WAMI) feature extraction is important for applications such as target tracking, traffic management and accident discovery. With the increasing amount of WAMI collections and feature extraction from the data, a scalable framework is needed to handle the large amount of information. Cloud computing is one of the approaches recently applied in large scale or big data. In this paper, MapReduce in Hadoop is investigated for large scale feature extraction tasks for WAMI. Specifically, a large dataset of WAMI images is divided into several splits. Each split has a small subset of WAMI images. The feature extractions of WAMI images in each split are distributed to slave nodes in the Hadoop system. Feature extraction of each image is performed individually in the assigned slave node. Finally, the feature extraction results are sent to the Hadoop File System (HDFS) to aggregate the feature information over the collected imagery. Experiments of feature extraction with and without MapReduce are conducted to illustrate the effectiveness of our proposed Cloud-Enabled WAMI Exploitation (CAWE) approach.

Feature-extracted joint transform correlation.

PubMed

Alam, M S

1995-12-10

A new technique for real-time optical character recognition that uses a joint transform correlator is proposed. This technique employs feature-extracted patterns for the reference image to detect a wide range of characters in one step. The proposed technique significantly enhances the processing speed when compared with the presently available joint transform correlator architectures and shows feasibility for multichannel joint transform correlation.

Facial bacterial infections: folliculitis.

PubMed

Laureano, Ana Cristina; Schwartz, Robert A; Cohen, Philip J

2014-01-01

Facial bacterial infections are most commonly caused by infections of the hair follicles. Wherever pilosebaceous units are found folliculitis can occur, with the most frequent bacterial culprit being Staphylococcus aureus. We review different origins of facial folliculitis, distinguishing bacterial forms from other infectious and non-infectious mimickers. We distinguish folliculitis from pseudofolliculitis and perifolliculitis. Clinical features, etiology, pathology, and management options are also discussed. Copyright © 2014. Published by Elsevier Inc.

Three-dimensional analysis of facial morphology.

PubMed

Liu, Yun; Kau, Chung How; Talbert, Leslie; Pan, Feng

2014-09-01

The objectives of this study were to evaluate sexual dimorphism for facial features within Chinese and African American populations and to compare the facial morphology by sex between these 2 populations. Three-dimensional facial images were acquired by using the portable 3dMDface System, which captured 189 subjects from 2 population groups of Chinese (n = 72) and African American (n = 117). Each population was categorized into male and female groups for evaluation. All subjects in the groups were aged between 18 and 30 years and had no apparent facial anomalies. A total of 23 anthropometric landmarks were identified on the three-dimensional faces of each subject. Twenty-one measurements in 4 regions, including 19 distances and 2 angles, were not only calculated but also compared within and between the Chinese and African American populations. The Student's t-test was used to analyze each data set obtained within each subgroup. Distinct facial differences were presented between the examined subgroups. When comparing the sex differences of facial morphology in the Chinese population, significant differences were noted in 71.43% of the parameters calculated, and the same proportion was found in the African American group. The facial morphologic differences between the Chinese and African American populations were evaluated by sex. The proportion of significant differences in the parameters calculated was 90.48% for females and 95.24% for males between the 2 populations. The African American population had a more convex profile and greater face width than those of the Chinese population. Sexual dimorphism for facial features was presented in both the Chinese and African American populations. In addition, there were significant differences in facial morphology between these 2 populations.

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized

Research on facial expression simulation based on depth image

NASA Astrophysics Data System (ADS)

Ding, Sha-sha; Duan, Jin; Zhao, Yi-wu; Xiao, Bo; Wang, Hao

2017-11-01

Nowadays, face expression simulation is widely used in film and television special effects, human-computer interaction and many other fields. Facial expression is captured by the device of Kinect camera .The method of AAM algorithm based on statistical information is employed to detect and track faces. The 2D regression algorithm is applied to align the feature points. Among them, facial feature points are detected automatically and 3D cartoon model feature points are signed artificially. The aligned feature points are mapped by keyframe techniques. In order to improve the animation effect, Non-feature points are interpolated based on empirical models. Under the constraint of Bézier curves we finish the mapping and interpolation. Thus the feature points on the cartoon face model can be driven if the facial expression varies. In this way the purpose of cartoon face expression simulation in real-time is came ture. The experiment result shows that the method proposed in this text can accurately simulate the facial expression. Finally, our method is compared with the previous method. Actual data prove that the implementation efficiency is greatly improved by our method.

Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

DTIC Science & Technology

1981-03-01

This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

ECG feature extraction and disease diagnosis.

PubMed

Bhyri, Channappa; Hamde, S T; Waghmare, L M

2011-01-01

An important factor to consider when using findings on electrocardiograms for clinical decision making is that the waveforms are influenced by normal physiological and technical factors as well as by pathophysiological factors. In this paper, we propose a method for the feature extraction and heart disease diagnosis using wavelet transform (WT) technique and LabVIEW (Laboratory Virtual Instrument Engineering workbench). LabVIEW signal processing tools are used to denoise the signal before applying the developed algorithm for feature extraction. First, we have developed an algorithm for R-peak detection using Haar wavelet. After 4th level decomposition of the ECG signal, the detailed coefficient is squared and the standard deviation of the squared detailed coefficient is used as the threshold for detection of R-peaks. Second, we have used daubechies (db6) wavelet for the low resolution signals. After cross checking the R-peak location in 4th level, low resolution signal of daubechies wavelet P waves and T waves are detected. Other features of diagnostic importance, mainly heart rate, R-wave width, Q-wave width, T-wave amplitude and duration, ST segment and frontal plane axis are also extracted and scoring pattern is applied for the purpose of heart disease diagnosis. In this study, detection of tachycardia, bradycardia, left ventricular hypertrophy, right ventricular hypertrophy and myocardial infarction have been considered. In this work, CSE ECG data base which contains 5000 samples recorded at a sampling frequency of 500 Hz and the ECG data base created by the S.G.G.S. Institute of Engineering and Technology, Nanded (Maharashtra) have been used.

Does Facial Amimia Impact the Recognition of Facial Emotions? An EMG Study in Parkinson’s Disease

PubMed Central

Argaud, Soizic; Delplanque, Sylvain; Houvenaghel, Jean-François; Auffret, Manon; Duprez, Joan; Vérin, Marc; Grandjean, Didier; Sauleau, Paul

2016-01-01

According to embodied simulation theory, understanding other people’s emotions is fostered by facial mimicry. However, studies assessing the effect of facial mimicry on the recognition of emotion are still controversial. In Parkinson’s disease (PD), one of the most distinctive clinical features is facial amimia, a reduction in facial expressiveness, but patients also show emotional disturbances. The present study used the pathological model of PD to examine the role of facial mimicry on emotion recognition by investigating EMG responses in PD patients during a facial emotion recognition task (anger, joy, neutral). Our results evidenced a significant decrease in facial mimicry for joy in PD, essentially linked to the absence of reaction of the zygomaticus major and the orbicularis oculi muscles in response to happy avatars, whereas facial mimicry for expressions of anger was relatively preserved. We also confirmed that PD patients were less accurate in recognizing positive and neutral facial expressions and highlighted a beneficial effect of facial mimicry on the recognition of emotion. We thus provide additional arguments for embodied simulation theory suggesting that facial mimicry is a potential lever for therapeutic actions in PD even if it seems not to be necessarily required in recognizing emotion as such. PMID:27467393

Sad Facial Expressions Increase Choice Blindness

PubMed Central

Wang, Yajie; Zhao, Song; Zhang, Zhijie; Feng, Wenfeng

2018-01-01

Previous studies have discovered a fascinating phenomenon known as choice blindness—individuals fail to detect mismatches between the face they choose and the face replaced by the experimenter. Although previous studies have reported a couple of factors that can modulate the magnitude of choice blindness, the potential effect of facial expression on choice blindness has not yet been explored. Using faces with sad and neutral expressions (Experiment 1) and faces with happy and neutral expressions (Experiment 2) in the classic choice blindness paradigm, the present study investigated the effects of facial expressions on choice blindness. The results showed that the detection rate was significantly lower on sad faces than neutral faces, whereas no significant difference was observed between happy faces and neutral faces. The exploratory analysis of verbal reports found that participants who reported less facial features for sad (as compared to neutral) expressions also tended to show a lower detection rate of sad (as compared to neutral) faces. These findings indicated that sad facial expressions increased choice blindness, which might have resulted from inhibition of further processing of the detailed facial features by the less attractive sad expressions (as compared to neutral expressions). PMID:29358926

Sad Facial Expressions Increase Choice Blindness.

PubMed

Wang, Yajie; Zhao, Song; Zhang, Zhijie; Feng, Wenfeng

2017-01-01

Previous studies have discovered a fascinating phenomenon known as choice blindness-individuals fail to detect mismatches between the face they choose and the face replaced by the experimenter. Although previous studies have reported a couple of factors that can modulate the magnitude of choice blindness, the potential effect of facial expression on choice blindness has not yet been explored. Using faces with sad and neutral expressions (Experiment 1) and faces with happy and neutral expressions (Experiment 2) in the classic choice blindness paradigm, the present study investigated the effects of facial expressions on choice blindness. The results showed that the detection rate was significantly lower on sad faces than neutral faces, whereas no significant difference was observed between happy faces and neutral faces. The exploratory analysis of verbal reports found that participants who reported less facial features for sad (as compared to neutral) expressions also tended to show a lower detection rate of sad (as compared to neutral) faces. These findings indicated that sad facial expressions increased choice blindness, which might have resulted from inhibition of further processing of the detailed facial features by the less attractive sad expressions (as compared to neutral expressions).

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham).

PubMed

Bukar, Ali M; Ugail, Hassan

2017-09-01

Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an active appearance model (AAM) to extract facial features from available images. An aging function is then modelled using sparse partial least squares regression (sPLS). Thereafter, the aging function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham's facial image that was taken when he was 21 months old to the ages of 6, 14, and 22 years. The algorithm presented in this study could potentially be used to enhance the search for missing people worldwide. © 2017 American Academy of Forensic Sciences.

Features extraction in anterior and posterior cruciate ligaments analysis.

PubMed

Zarychta, P

2015-12-01

The main aim of this research is finding the feature vectors of the anterior and posterior cruciate ligaments (ACL and PCL). These feature vectors have to clearly define the ligaments structure and make it easier to diagnose them. Extraction of feature vectors is obtained by analysis of both anterior and posterior cruciate ligaments. This procedure is performed after the extraction process of both ligaments. In the first stage in order to reduce the area of analysis a region of interest including cruciate ligaments (CL) is outlined in order to reduce the area of analysis. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges has been implemented. After finding the region of interest (ROI), the fuzzy connectedness procedure is performed. This procedure permits to extract the anterior and posterior cruciate ligament structures. In the last stage, on the basis of the extracted anterior and posterior cruciate ligament structures, 3-dimensional models of the anterior and posterior cruciate ligament are built and the feature vectors created. This methodology has been implemented in MATLAB and tested on clinical T1-weighted magnetic resonance imaging (MRI) slices of the knee joint. The 3D display is based on the Visualization Toolkit (VTK). Copyright © 2015 Elsevier Ltd. All rights reserved.

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2005-01-01

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, re-circulation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; isc-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2004-01-01

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, recirculation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; iso-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for (co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Tool Wear Feature Extraction Based on Hilbert Marginal Spectrum

NASA Astrophysics Data System (ADS)

Guan, Shan; Song, Weijie; Pang, Hongyang

2017-09-01

In the metal cutting process, the signal contains a wealth of tool wear state information. A tool wear signal’s analysis and feature extraction method based on Hilbert marginal spectrum is proposed. Firstly, the tool wear signal was decomposed by empirical mode decomposition algorithm and the intrinsic mode functions including the main information were screened out by the correlation coefficient and the variance contribution rate. Secondly, Hilbert transform was performed on the main intrinsic mode functions. Hilbert time-frequency spectrum and Hilbert marginal spectrum were obtained by Hilbert transform. Finally, Amplitude domain indexes were extracted on the basis of the Hilbert marginal spectrum and they structured recognition feature vector of tool wear state. The research results show that the extracted features can effectively characterize the different wear state of the tool, which provides a basis for monitoring tool wear condition.

Combined rule extraction and feature elimination in supervised classification.

PubMed

Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E

2012-09-01

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

Automated Extraction of Secondary Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne M.; Haimes, Robert

2005-01-01

The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.

Multiple feature extraction by using simultaneous wavelet transforms

NASA Astrophysics Data System (ADS)

Mazzaferri, Javier; Ledesma, Silvia; Iemmi, Claudio

2003-07-01

We propose here a method to optically perform multiple feature extraction by using wavelet transforms. The method is based on obtaining the optical correlation by means of a Vander Lugt architecture, where the scene and the filter are displayed on spatial light modulators (SLMs). Multiple phase filters containing the information about the features that we are interested in extracting are designed and then displayed on an SLM working in phase mostly mode. We have designed filters to simultaneously detect edges and corners or different characteristic frequencies contained in the input scene. Simulated and experimental results are shown.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Augmentation of linear facial anthropometrics through modern morphometrics: a facial convexity example.

PubMed

Wei, R; Claes, P; Walters, M; Wholley, C; Clement, J G

2011-06-01

The facial region has traditionally been quantified using linear anthropometrics. These are well established in dentistry, but require expertise to be used effectively. The aim of this study was to augment the utility of linear anthropometrics by applying them in conjunction with modern 3-D morphometrics. Facial images of 75 males and 94 females aged 18-25 years with self-reported Caucasian ancestry were used. An anthropometric mask was applied to establish corresponding quasi-landmarks on the images in the dataset. A statistical face-space, encoding shape covariation, was established. The facial median plane was extracted facilitating both manual and automated indication of commonly used midline landmarks. From both indications, facial convexity angles were calculated and compared. The angles were related to the face-space using a regression based pathway enabling the visualization of facial form associated with convexity variation. Good agreement between the manual and automated angles was found (Pearson correlation: 0.9478-0.9474, Dahlberg root mean squared error: 1.15°-1.24°). The population mean angle was 166.59°-166.29° (SD 5.09°-5.2°) for males-females. The angle-pathway provided valuable feedback. Linear facial anthropometrics can be extended when used in combination with a face-space derived from 3-D scans and the exploration of property pathways inferred in a statistically verifiable way. © 2011 Australian Dental Association.

Human (Homo sapiens) facial attractiveness in relation to skin texture and color.

PubMed

Fink, B; Grammer, K; Thornhill, R

2001-03-01

The notion that surface texture may provide important information about the geometry of visible surfaces has attracted considerable attention for a long time. The present study shows that skin texture plays a significant role in the judgment of female facial beauty. Following research in clinical dermatology, the authors developed a computer program that implemented an algorithm based on co-occurrence matrices for the analysis of facial skin texture. Homogeneity and contrast features as well as color parameters were extracted out of stimulus faces. Attractiveness ratings of the images made by male participants relate positively to parameters of skin homogeneity. The authors propose that skin texture is a cue to fertility and health. In contrast to some previous studies, the authors found that dark skin, not light skin, was rated as most attractive.

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

Facial Orientation and Facial Shape in Extant Great Apes: A Geometric Morphometric Analysis of Covariation

PubMed Central

Neaux, Dimitri; Guy, Franck; Gilissen, Emmanuel; Coudyzer, Walter; Vignaud, Patrick; Ducrocq, Stéphane

2013-01-01

The organization of the bony face is complex, its morphology being influenced in part by the rest of the cranium. Characterizing the facial morphological variation and craniofacial covariation patterns in extant hominids is fundamental to the understanding of their evolutionary history. Numerous studies on hominid facial shape have proposed hypotheses concerning the relationship between the anterior facial shape, facial block orientation and basicranial flexion. In this study we test these hypotheses in a sample of adult specimens belonging to three extant hominid genera (Homo, Pan and Gorilla). Intraspecific variation and covariation patterns are analyzed using geometric morphometric methods and multivariate statistics, such as partial least squared on three-dimensional landmarks coordinates. Our results indicate significant intraspecific covariation between facial shape, facial block orientation and basicranial flexion. Hominids share similar characteristics in the relationship between anterior facial shape and facial block orientation. Modern humans exhibit a specific pattern in the covariation between anterior facial shape and basicranial flexion. This peculiar feature underscores the role of modern humans' highly-flexed basicranium in the overall integration of the cranium. Furthermore, our results are consistent with the hypothesis of a relationship between the reduction of the value of the cranial base angle and a downward rotation of the facial block in modern humans, and to a lesser extent in chimpanzees. PMID:23441232

Thermal imaging as a biometrics approach to facial signature authentication.

PubMed

Guzman, A M; Goryawala, M; Wang, Jin; Barreto, A; Andrian, J; Rishe, N; Adjouadi, M

2013-01-01

A new thermal imaging framework with unique feature extraction and similarity measurements for face recognition is presented. The research premise is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The proposed algorithm is fully integrated and consolidates the critical steps of feature extraction through the use of morphological operators, registration using the Linear Image Registration Tool and matching through unique similarity measures designed for this task. The novel approach at developing a thermal signature template using four images taken at various instants of time ensured that unforeseen changes in the vasculature over time did not affect the biometric matching process as the authentication process relied only on consistent thermal features. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed an average accuracy of 88.46% for skeletonized signatures and 90.39% for anisotropically diffused signatures. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal imaging based systems. Empirical results applying this approach to an existing database of thermal images proves this assertion.

Down syndrome detection from facial photographs using machine learning techniques

NASA Astrophysics Data System (ADS)

Zhao, Qian; Rosenbaum, Kenneth; Sze, Raymond; Zand, Dina; Summar, Marshall; Linguraru, Marius George

2013-02-01

Down syndrome is the most commonly occurring chromosomal condition; one in every 691 babies in United States is born with it. Patients with Down syndrome have an increased risk for heart defects, respiratory and hearing problems and the early detection of the syndrome is fundamental for managing the disease. Clinically, facial appearance is an important indicator in diagnosing Down syndrome and it paves the way for computer-aided diagnosis based on facial image analysis. In this study, we propose a novel method to detect Down syndrome using photography for computer-assisted image-based facial dysmorphology. Geometric features based on facial anatomical landmarks, local texture features based on the Contourlet transform and local binary pattern are investigated to represent facial characteristics. Then a support vector machine classifier is used to discriminate normal and abnormal cases; accuracy, precision and recall are used to evaluate the method. The comparison among the geometric, local texture and combined features was performed using the leave-one-out validation. Our method achieved 97.92% accuracy with high precision and recall for the combined features; the detection results were higher than using only geometric or texture features. The promising results indicate that our method has the potential for automated assessment for Down syndrome from simple, noninvasive imaging data.

Occlusal and facial features in Amazon indigenous: An insight into the role of genetics and environment in the etiology dental malocclusion.

PubMed

de Souza, Bento Sousa; Bichara, Livia Monteiro; Guerreiro, João Farias; Quintão, Cátia Cardoso Abdo; Normando, David

2015-09-01

Indigenous people of the Xingu river present a similar tooth wear pattern, practise exclusive breast-feeding, no pacifier use, and have a large intertribal genetic distance. To revisit the etiology of dental malocclusion features considering these population characteristics. Occlusion and facial features of five semi-isolated Amazon indigenous populations (n=351) were evaluated and compared to previously published data from urban Amazon people. Malocclusion prevalence ranged from 33.8% to 66.7%. Overall this prevalence is lower when compared to urban people mainly regarding posterior crossbite. A high intertribal diversity was found. The Arara-Laranjal village had a population with a normal face profile (98%) and a high rate of normal occlusion (66.2%), while another group from the same ethnicity presented a high prevalence of malocclusion, the highest occurrence of Class III malocclusion (32.6%) and long face (34.8%). In Pat-Krô village the population had the highest prevalence of Class II malocclusion (43.9%), convex profile (38.6%), increased overjet (36.8%) and deep bite (15.8%). Another village's population, from the same ethnicity, had a high frequency of anterior open bite (22.6%) and anterior crossbite (12.9%). The highest occurrence of bi-protrusion was found in the group with the lowest prevalence of dental crowding, and vice versa. Supported by previous genetic studies and given their similar environmental conditions, the high intertribal diversity of occlusal and facial features suggests that genetic factors contribute substantially to the morphology of occlusal and facial features in the indigenous groups studied. The low prevalence of posterior crossbite in the remote indigenous populations compared with urban populations may relate to prolonged breastfeeding and an absence of pacifiers in the indigenous groups. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automation of lidar-based hydrologic feature extraction workflows using GIS

NASA Astrophysics Data System (ADS)

Borlongan, Noel Jerome B.; de la Cruz, Roel M.; Olfindo, Nestor T.; Perez, Anjillyn Mae C.

2016-10-01

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

iFeature: a python package and web server for features extraction and selection from protein and peptide sequences.

PubMed

Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, André; Marquez-Lago, Tatiana T; Wang, Yanan; Webb, Geoffrey I; Smith, A Ian; Daly, Roger J; Chou, Kuo-Chen; Song, Jiangning

2018-03-08

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection, and dimensionality reduction algorithms, greatly facilitating training, analysis, and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. jiangning.song@monash.edu; kcchou@gordonlifescience.org; roger.daly@monash.edu. Supplementary data are available at Bioinformatics online.

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

PubMed Central

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-01-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate. PMID:24696801

Shape adaptive, robust iris feature extraction from noisy iris images.

PubMed

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Feature extraction via KPCA for classification of gait patterns.

PubMed

Wu, Jianning; Wang, Jue; Liu, Li

2007-06-01

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

Investigation of automated feature extraction using multiple data sources

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Perkins, Simon J.; Pope, Paul A.; Theiler, James P.; David, Nancy A.; Porter, Reid B.

2003-04-01

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

Spectrum of mucocutaneous, ocular and facial features and delineation of novel presentations in 62 classical Ehlers-Danlos syndrome patients.

PubMed

Colombi, M; Dordoni, C; Venturini, M; Ciaccio, C; Morlino, S; Chiarelli, N; Zanca, A; Calzavara-Pinton, P; Zoppi, N; Castori, M; Ritelli, M

2017-12-01

Classical Ehlers-Danlos syndrome (cEDS) is characterized by marked cutaneous involvement, according to the Villefranche nosology and its 2017 revision. However, the diagnostic flow-chart that prompts molecular testing is still based on experts' opinion rather than systematic published data. Here we report on 62 molecularly characterized cEDS patients with focus on skin, mucosal, facial, and articular manifestations. The major and minor Villefranche criteria, additional 11 mucocutaneous signs and 15 facial dysmorphic traits were ascertained and feature rates compared by sex and age. In our cohort, we did not observe any mandatory clinical sign. Skin hyperextensibility plus atrophic scars was the most frequent combination, whereas generalized joint hypermobility according to the Beighton score decreased with age. Skin was more commonly hyperextensible on elbows, neck, and knees. The sites more frequently affected by abnormal atrophic scarring were knees, face (especially forehead), pretibial area, and elbows. Facial dysmorphism commonly affected midface/orbital areas with epicanthal folds and infraorbital creases more commonly observed in young patients. Our findings suggest that the combination of ≥1 eye dysmorphism and facial/forehead scars may support the diagnosis in children. Minor acquired traits, such as molluscoid pseudotumors, subcutaneous spheroids, and signs of premature skin aging are equally useful in adults. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Automated diagnosis of fetal alcohol syndrome using 3D facial image analysis

PubMed Central

Fang, Shiaofen; McLaughlin, Jason; Fang, Jiandong; Huang, Jeffrey; Autti-Rämö, Ilona; Fagerlund, Åse; Jacobson, Sandra W.; Robinson, Luther K.; Hoyme, H. Eugene; Mattson, Sarah N.; Riley, Edward; Zhou, Feng; Ward, Richard; Moore, Elizabeth S.; Foroud, Tatiana

2012-01-01

Objectives Use three-dimensional (3D) facial laser scanned images from children with fetal alcohol syndrome (FAS) and controls to develop an automated diagnosis technique that can reliably and accurately identify individuals prenatally exposed to alcohol. Methods A detailed dysmorphology evaluation, history of prenatal alcohol exposure, and 3D facial laser scans were obtained from 149 individuals (86 FAS; 63 Control) recruited from two study sites (Cape Town, South Africa and Helsinki, Finland). Computer graphics, machine learning, and pattern recognition techniques were used to automatically identify a set of facial features that best discriminated individuals with FAS from controls in each sample. Results An automated feature detection and analysis technique was developed and applied to the two study populations. A unique set of facial regions and features were identified for each population that accurately discriminated FAS and control faces without any human intervention. Conclusion Our results demonstrate that computer algorithms can be used to automatically detect facial features that can discriminate FAS and control faces. PMID:18713153

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Facial contrast is a cue for perceiving health from the face.

PubMed

Russell, Richard; Porcheron, Aurélie; Sweda, Jennifer R; Jones, Alex L; Mauger, Emmanuelle; Morizot, Frederique

2016-09-01

How healthy someone appears has important social consequences. Yet the visual cues that determine perceived health remain poorly understood. Here we report evidence that facial contrast-the luminance and color contrast between internal facial features and the surrounding skin-is a cue for the perception of health from the face. Facial contrast was measured from a large sample of Caucasian female faces, and was found to predict ratings of perceived health. Most aspects of facial contrast were positively related to perceived health, meaning that faces with higher facial contrast appeared healthier. In 2 subsequent experiments, we manipulated facial contrast and found that participants perceived faces with increased facial contrast as appearing healthier than faces with decreased facial contrast. These results support the idea that facial contrast is a cue for perceived health. This finding adds to the growing knowledge about perceived health from the face, and helps to ground our understanding of perceived health in terms of lower-level perceptual features such as contrast. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

Compact and Hybrid Feature Description for Building Extraction

NASA Astrophysics Data System (ADS)

Li, Z.; Liu, Y.; Hu, Y.; Li, P.; Ding, Y.

2017-05-01

Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.

Clinical features and management of facial nerve paralysis in children: analysis of 24 cases.

PubMed

Cha, H E; Baek, M K; Yoon, J H; Yoon, B K; Kim, M J; Lee, J H

2010-04-01

To evaluate the causes, treatment modalities and recovery rate of paediatric facial nerve paralysis. We analysed 24 cases of paediatric facial nerve paralysis diagnosed in the otolaryngology department of Gachon University Gil Medical Center between January 2001 and June 2006. The most common cause was idiopathic palsy (16 cases, 66.7 per cent). The most common degree of facial nerve paralysis on first presentation was House-Brackmann grade IV (15 of 24 cases). All cases were treated with steroids. One of the 24 cases was also treated surgically with facial nerve decompression. Twenty-two cases (91.6 per cent) recovered to House-Brackmann grade I or II over the six-month follow-up period. Facial nerve paralysis in children can generally be successfully treated with conservative measures. However, in cases associated with trauma, radiological investigation is required for further evaluation and treatment.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

PubMed Central

Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Face recognition algorithm using extended vector quantization histogram features.

PubMed

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

Cone beam tomographic study of facial structures characteristics at rest and wide smile, and their correlation with the facial types.

PubMed

Martins, Luciana Flaquer; Vigorito, Julio Wilson

2013-01-01

To determine the characteristics of facial soft tissues at rest and wide smile, and their possible relation to the facial type. We analyzed a sample of forty-eight young female adults, aged between 19.10 and 40 years old, with a mean age of 30.9 years, who had balanced profile and passive lip seal. Cone beam computed tomographies were performed at rest and wide smile postures on the entire sample which was divided into three groups according to individual facial types. Soft tissue features analysis of the lips, nose, zygoma and chin were done in sagittal, axial and frontal axis tomographic views. No differences were observed in any of the facial type variables for the static analysis of facial structures at both rest and wide smile postures. Dynamic analysis showed that brachifacial types are more sensitive to movement, presenting greater sagittal lip contraction. However, the lip movement produced by this type of face results in a narrow smile, with smaller tooth exposure area when compared with other facial types. Findings pointed out that the position of the upper lip should be ahead of the lower lip, and the latter, ahead of the pogonion. It was also found that the facial type does not impact the positioning of these structures. Additionally, the use of cone beam computed tomography may be a valuable method to study craniofacial features.

Spoofing detection on facial images recognition using LBP and GLCM combination

NASA Astrophysics Data System (ADS)

Sthevanie, F.; Ramadhani, K. N.

2018-03-01

The challenge for the facial based security system is how to detect facial image falsification such as facial image spoofing. Spoofing occurs when someone try to pretend as a registered user to obtain illegal access and gain advantage from the protected system. This research implements facial image spoofing detection method by analyzing image texture. The proposed method for texture analysis combines the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) method. The experimental results show that spoofing detection using LBP and GLCM combination achieves high detection rate compared to that of using only LBP feature or GLCM feature.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

NASA Astrophysics Data System (ADS)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

Facial measurements for frame design.

PubMed

Tang, C Y; Tang, N; Stewart, M C

1998-04-01

Anthropometric data for the purpose of spectacle frame design are scarce in the literature. Definitions of facial features to be measured with existing systems of facial measurement are often not specific enough for frame design and manufacturing. Currently, for individual frame design, experienced personnel collect data with facial rules or instruments. A new measuring system is proposed, making use of a template in the form of a spectacle frame. Upon fitting the template onto a subject, most of the measuring references can be defined. Such a system can be administered by lesser-trained personnel and can be used for researches covering a larger population.

Feature extraction applied to agricultural crops as seen by LANDSAT

NASA Technical Reports Server (NTRS)

Kauth, R. J.; Lambeck, P. F.; Richardson, W.; Thomas, G. S.; Pentland, A. P. (Principal Investigator)

1979-01-01

The physical interpretation of the spectral-temporal structure of LANDSAT data can be conveniently described in terms of a graphic descriptive model called the Tassled Cap. This model has been a source of development not only in crop-related feature extraction, but also for data screening and for haze effects correction. Following its qualitative description and an indication of its applications, the model is used to analyze several feature extraction algorithms.

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

NASA Astrophysics Data System (ADS)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

De novo pathogenic variants in CHAMP1 are associated with global developmental delay, intellectual disability, and dysmorphic facial features.

PubMed

Tanaka, Akemi J; Cho, Megan T; Retterer, Kyle; Jones, Julie R; Nowak, Catherine; Douglas, Jessica; Jiang, Yong-Hui; McConkie-Rosell, Allyn; Schaefer, G Bradley; Kaylor, Julie; Rahman, Omar A; Telegrafi, Aida; Friedman, Bethany; Douglas, Ganka; Monaghan, Kristin G; Chung, Wendy K

2016-01-01

We identified five unrelated individuals with significant global developmental delay and intellectual disability (ID), dysmorphic facial features and frequent microcephaly, and de novo predicted loss-of-function variants in chromosome alignment maintaining phosphoprotein 1 (CHAMP1). Our findings are consistent with recently reported de novo mutations in CHAMP1 in five other individuals with similar features. CHAMP1 is a zinc finger protein involved in kinetochore-microtubule attachment and is required for regulating the proper alignment of chromosomes during metaphase in mitosis. Mutations in CHAMP1 may affect cell division and hence brain development and function, resulting in developmental delay and ID.

Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

PubMed

Ayinde, Babajide O; Zurada, Jacek M

2017-09-01

This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features. It is also shown that autoencoders with nonnegativity constraints on weights are capable of extracting fewer redundant features than conventional sparse autoencoders. The concept is illustrated using conventional sparse autoencoder and nonnegativity-constrained autoencoders with MNIST digits recognition, NORB normalized-uniform object data and Yale face dataset. Copyright © 2017 Elsevier Ltd. All rights reserved.

A window-based time series feature extraction method.

PubMed

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Facial paralysis for the plastic surgeon.

PubMed

Kosins, Aaron M; Hurvitz, Keith A; Evans, Gregory Rd; Wirth, Garrett A

2007-01-01

Facial paralysis presents a significant and challenging reconstructive problem for plastic surgeons. An aesthetically pleasing and acceptable outcome requires not only good surgical skills and techniques, but also knowledge of facial nerve anatomy and an understanding of the causes of facial paralysis.The loss of the ability to move the face has both social and functional consequences for the patient. At the Facial Palsy Clinic in Edinburgh, Scotland, 22,954 patients were surveyed, and over 50% were found to have a considerable degree of psychological distress and social withdrawal as a consequence of their facial paralysis. Functionally, patients present with unilateral or bilateral loss of voluntary and nonvoluntary facial muscle movements. Signs and symptoms can include an asymmetric smile, synkinesis, epiphora or dry eye, abnormal blink, problems with speech articulation, drooling, hyperacusis, change in taste and facial pain.With respect to facial paralysis, surgeons tend to focus on the surgical, or 'hands-on', aspect. However, it is believed that an understanding of the disease process is equally (if not more) important to a successful surgical outcome. The purpose of the present review is to describe the anatomy and diagnostic patterns of the facial nerve, and the epidemiology and common causes of facial paralysis, including clinical features and diagnosis. Treatment options for paralysis are vast, and may include nerve decompression, facial reanimation surgery and botulinum toxin injection, but these are beyond the scope of the present paper.

Facial paralysis for the plastic surgeon

PubMed Central

Kosins, Aaron M; Hurvitz, Keith A; Evans, Gregory RD; Wirth, Garrett A

2007-01-01

Facial paralysis presents a significant and challenging reconstructive problem for plastic surgeons. An aesthetically pleasing and acceptable outcome requires not only good surgical skills and techniques, but also knowledge of facial nerve anatomy and an understanding of the causes of facial paralysis. The loss of the ability to move the face has both social and functional consequences for the patient. At the Facial Palsy Clinic in Edinburgh, Scotland, 22,954 patients were surveyed, and over 50% were found to have a considerable degree of psychological distress and social withdrawal as a consequence of their facial paralysis. Functionally, patients present with unilateral or bilateral loss of voluntary and nonvoluntary facial muscle movements. Signs and symptoms can include an asymmetric smile, synkinesis, epiphora or dry eye, abnormal blink, problems with speech articulation, drooling, hyperacusis, change in taste and facial pain. With respect to facial paralysis, surgeons tend to focus on the surgical, or ‘hands-on’, aspect. However, it is believed that an understanding of the disease process is equally (if not more) important to a successful surgical outcome. The purpose of the present review is to describe the anatomy and diagnostic patterns of the facial nerve, and the epidemiology and common causes of facial paralysis, including clinical features and diagnosis. Treatment options for paralysis are vast, and may include nerve decompression, facial reanimation surgery and botulinum toxin injection, but these are beyond the scope of the present paper. PMID:19554190

Principal component analysis for surface reflection components and structure in facial images and synthesis of facial images for various ages

NASA Astrophysics Data System (ADS)

Hirose, Misa; Toyota, Saori; Ojima, Nobutoshi; Ogawa-Ochiai, Keiko; Tsumura, Norimichi

2017-08-01

In this paper, principal component analysis is applied to the distribution of pigmentation, surface reflectance, and landmarks in whole facial images to obtain feature values. The relationship between the obtained feature vectors and the age of the face is then estimated by multiple regression analysis so that facial images can be modulated for woman aged 10-70. In a previous study, we analyzed only the distribution of pigmentation, and the reproduced images appeared to be younger than the apparent age of the initial images. We believe that this happened because we did not modulate the facial structures and detailed surfaces, such as wrinkles. By considering landmarks and surface reflectance over the entire face, we were able to analyze the variation in the distributions of facial structures and fine asperity, and pigmentation. As a result, our method is able to appropriately modulate the appearance of a face so that it appears to be the correct age.

Optical character recognition with feature extraction and associative memory matrix

NASA Astrophysics Data System (ADS)

Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

1998-06-01

A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

Coding visual features extracted from video sequences.

PubMed

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Stimulus encoding and feature extraction by multiple sensory neurons.

PubMed

Krahe, Rüdiger; Kreiman, Gabriel; Gabbiani, Fabrizio; Koch, Christof; Metzner, Walter

2002-03-15

Neighboring cells in topographical sensory maps may transmit similar information to the next higher level of processing. How information transmission by groups of nearby neurons compares with the performance of single cells is a very important question for understanding the functioning of the nervous system. To tackle this problem, we quantified stimulus-encoding and feature extraction performance by pairs of simultaneously recorded electrosensory pyramidal cells in the hindbrain of weakly electric fish. These cells constitute the output neurons of the first central nervous stage of electrosensory processing. Using random amplitude modulations (RAMs) of a mimic of the fish's own electric field within behaviorally relevant frequency bands, we found that pyramidal cells with overlapping receptive fields exhibit strong stimulus-induced correlations. To quantify the encoding of the RAM time course, we estimated the stimuli from simultaneously recorded spike trains and found significant improvements over single spike trains. The quality of stimulus reconstruction, however, was still inferior to the one measured for single primary sensory afferents. In an analysis of feature extraction, we found that spikes of pyramidal cell pairs coinciding within a time window of a few milliseconds performed significantly better at detecting upstrokes and downstrokes of the stimulus compared with isolated spikes and even spike bursts of single cells. Coincident spikes can thus be considered "distributed bursts." Our results suggest that stimulus encoding by primary sensory afferents is transformed into feature extraction at the next processing stage. There, stimulus-induced coincident activity can improve the extraction of behaviorally relevant features from the stimulus.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

On-line object feature extraction for multispectral scene representation

NASA Technical Reports Server (NTRS)

Ghassemian, Hassan; Landgrebe, David

1988-01-01

A new on-line unsupervised object-feature extraction method is presented that reduces the complexity and costs associated with the analysis of the multispectral image data and data transmission, storage, archival and distribution. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. The unity relation was defined that must exist among the pixels of an object. Automatic Multispectral Image Compaction Algorithm (AMICA) uses the within object pixel-feature gradient vector as a valuable contextual information to construct the object's features, which preserve the class separability information within the data. For on-line object extraction the path-hypothesis and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated.

Quantitative assessment of the facial features of a Mexican population dataset.

PubMed

Farrera, Arodi; García-Velasco, Maria; Villanueva, Maria

2016-05-01

The present study describes the morphological variation of a large database of facial photographs. The database comprises frontal (386 female, 764 males) and lateral (312 females, 666 males) images of Mexican individuals aged 14-69 years that were obtained under controlled conditions. We used geometric morphometric methods and multivariate statistics to describe the phenotypic variation within the dataset as well as the variation regarding sex and age groups. In addition, we explored the correlation between facial traits in both views. We found a spectrum of variation that encompasses broad and narrow faces. In frontal view, the latter is associated to a longer nose, a thinner upper lip, a shorter lower face and to a longer upper face, than individuals with broader faces. In lateral view, antero-posteriorly shortened faces are associated to a longer profile and to a shortened helix, than individuals with longer faces. Sexual dimorphism is found in all age groups except for individuals above 39 years old in lateral view. Likewise, age-related changes are significant for both sexes, except for females above 29 years old in both views. Finally, we observed that the pattern of covariation between views differs in males and females mainly in the thickness of the upper lip and the angle of the facial profile and the auricle. The results of this study could contribute to the forensic practices as a complement for the construction of biological profiles, for example, to improve facial reconstruction procedures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

[Infantile facial paralysis: diagnostic and therapeutic features].

PubMed

Montalt, J; Barona, R; Comeche, C; Basterra, J

2000-01-01

This paper deals with a series of 11 cases of peripheral unilateral facial paralyses affecting children under 15 years. Following parameters are reviewed: age, sex, side immobilized, origin, morbid antecedents, clinical and neurophysiological explorations (electroneurography through magnetic stimulation) and the evolutive course of the cases. These items are assembled in 3 sketches in the article. Clinical assessment of face movility is more difficult as the patient is younger, nevertheless electroneurography was possible in the whole group. Clinical restoration was complete, excepting one complicated cholesteatomatous patient. Some aspects concerning the etiology, diagnostic explorations and management of each pediatric case are discussed.

Utility of optical facial feature and arm movement tracking systems to enable text communication in critically ill patients who cannot otherwise communicate.

PubMed

Muthuswamy, M B; Thomas, B N; Williams, D; Dingley, J

2014-09-01

Patients recovering from critical illness especially those with critical illness related neuropathy, myopathy, or burns to face, arms and hands are often unable to communicate by writing, speech (due to tracheostomy) or lip reading. This may frustrate both patient and staff. Two low cost movement tracking systems based around a laptop webcam and a laser/optical gaming system sensor were utilised as control inputs for on-screen text creation software and both were evaluated as communication tools in volunteers. Two methods were used to control an on-screen cursor to create short sentences via an on-screen keyboard: (i) webcam-based facial feature tracking, (ii) arm movement tracking by laser/camera gaming sensor and modified software. 16 volunteers with simulated tracheostomy and bandaged arms to simulate communication via gross movements of a burned limb, communicated 3 standard messages using each system (total 48 per system) in random sequence. Ten and 13 minor typographical errors occurred with each system respectively, however all messages were comprehensible. Speed of sentence formation ranged from 58 to 120s with the facial feature tracking system, and 60-160s with the arm movement tracking system. The average speed of sentence formation was 81s (range 58-120) and 104s (range 60-160) for facial feature and arm tracking systems respectively, (P<0.001, 2-tailed independent sample t-test). Both devices may be potentially useful communication aids in patients in general and burns critical care units who cannot communicate by conventional means, due to the nature of their injuries. Copyright © 2014 Elsevier Ltd and ISBI. All rights reserved.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Facial emotion perception impairments in schizophrenia patients with comorbid antisocial personality disorder.

PubMed

Tang, Dorothy Y Y; Liu, Amy C Y; Lui, Simon S Y; Lam, Bess Y H; Siu, Bonnie W M; Lee, Tatia M C; Cheung, Eric F C

2016-02-28

Impairment in facial emotion perception is believed to be associated with aggression. Schizophrenia patients with antisocial features are more impaired in facial emotion perception than their counterparts without these features. However, previous studies did not define the comorbidity of antisocial personality disorder (ASPD) using stringent criteria. We recruited 30 participants with dual diagnoses of ASPD and schizophrenia, 30 participants with schizophrenia and 30 controls. We employed the Facial Emotional Recognition paradigm to measure facial emotion perception, and administered a battery of neurocognitive tests. The Life History of Aggression scale was used. ANOVAs and ANCOVAs were conducted to examine group differences in facial emotion perception, and control for the effect of other neurocognitive dysfunctions on facial emotion perception. Correlational analyses were conducted to examine the association between facial emotion perception and aggression. Patients with dual diagnoses performed worst in facial emotion perception among the three groups. The group differences in facial emotion perception remained significant, even after other neurocognitive impairments were controlled for. Severity of aggression was correlated with impairment in perceiving negative-valenced facial emotions in patients with dual diagnoses. Our findings support the presence of facial emotion perception impairment and its association with aggression in schizophrenia patients with comorbid ASPD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Recent Advances in Face Lift to Achieve Facial Balance.

PubMed

Ilankovan, Velupillai

2017-03-01

Facial balance is achieved by correction of facial proportions and the facial contour. Ageing affects this balance in addition to other factors. We have strived to inform all the recent advances in providing this balance. The anatomy of ageing including various changed in clinical features are described. The procedures are explained on the basis of the upper, middle and lower face. Different face lift, neck lift procedures with innovative techniques are demonstrated. The aim is to provide an unoperated balanced facial proportion with zero complication.

Impaired recognition of facial emotions from low-spatial frequencies in Asperger syndrome.

PubMed

Kätsyri, Jari; Saalasti, Satu; Tiippana, Kaisa; von Wendt, Lennart; Sams, Mikko

2008-01-01

The theory of 'weak central coherence' [Happe, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(1), 5-25] implies that persons with autism spectrum disorders (ASDs) have a perceptual bias for local but not for global stimulus features. The recognition of emotional facial expressions representing various different levels of detail has not been studied previously in ASDs. We analyzed the recognition of four basic emotional facial expressions (anger, disgust, fear and happiness) from low-spatial frequencies (overall global shapes without local features) in adults with an ASD. A group of 20 participants with Asperger syndrome (AS) was compared to a group of non-autistic age- and sex-matched controls. Emotion recognition was tested from static and dynamic facial expressions whose spatial frequency contents had been manipulated by low-pass filtering at two levels. The two groups recognized emotions similarly from non-filtered faces and from dynamic vs. static facial expressions. In contrast, the participants with AS were less accurate than controls in recognizing facial emotions from very low-spatial frequencies. The results suggest intact recognition of basic facial emotions and dynamic facial information, but impaired visual processing of global features in ASDs.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

Kernel-based discriminant feature extraction using a representative dataset

NASA Astrophysics Data System (ADS)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

Multiple Mechanisms in the Perception of Face Gender: Effect of Sex-Irrelevant Features

ERIC Educational Resources Information Center

Komori, Masashi; Kawamura, Satoru; Ishihara, Shigekazu

2011-01-01

Effects of sex-relevant and sex-irrelevant facial features on the evaluation of facial gender were investigated. Participants rated masculinity of 48 male facial photographs and femininity of 48 female facial photographs. Eighty feature points were measured on each of the facial photographs. Using a generalized Procrustes analysis, facial shapes…

Contributions of individual face features to face discrimination.

PubMed

Logan, Andrew J; Gordon, Gael E; Loffler, Gunter

2017-08-01

Faces are highly complex stimuli that contain a host of information. Such complexity poses the following questions: (a) do observers exhibit preferences for specific information? (b) how does sensitivity to individual face parts compare? These questions were addressed by quantifying sensitivity to different face features. Discrimination thresholds were determined for synthetic faces under the following conditions: (i) 'full face': all face features visible; (ii) 'isolated feature': single feature presented in isolation; (iii) 'embedded feature': all features visible, but only one feature modified. Mean threshold elevations for isolated features, relative to full-faces, were 0.84x, 1.08, 2.12, 3.34, 4.07 and 4.47 for head-shape, hairline, nose, mouth, eyes and eyebrows respectively. Hence, when two full faces can be discriminated at threshold, the difference between the eyes is about four times less than what is required when discriminating between isolated eyes. In all cases, sensitivity was higher when features were presented in isolation than when they were embedded within a face context (threshold elevations of 0.94x, 1.74, 2.67, 2.90, 5.94 and 9.94). This reveals a specific pattern of sensitivity to face information. Observers are between two and four times more sensitive to external than internal features. The pattern for internal features (higher sensitivity for the nose, compared to mouth, eyes and eyebrows) is consistent with lower sensitivity for those parts affected by facial dynamics (e.g. facial expressions). That isolated features are easier to discriminate than embedded features supports a holistic face processing mechanism which impedes extraction of information about individual features from full faces. Copyright © 2017 Elsevier Ltd. All rights reserved.

Feature Extraction Using an Unsupervised Neural Network

DTIC Science & Technology

1991-05-03

with this neural netowrk is given and its connection to exploratory projection pursuit methods is established. DD I 2 P JA d 73 EDITIONj Of I NOV 6s...IS OBSOLETE $IN 0102- LF- 014- 6601 SECURITY CLASSIFICATION OF THIS PAGE (When Daoes Enlered) Feature Extraction using an Unsupervised Neural Network

A multi-approach feature extractions for iris recognition

NASA Astrophysics Data System (ADS)

Sanpachai, H.; Settapong, M.

2014-04-01

Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

A Study of Feature Extraction Using Divergence Analysis of Texture Features

NASA Technical Reports Server (NTRS)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Deep facial analysis: A new phase I epilepsy evaluation using computer vision.

PubMed

Ahmedt-Aristizabal, David; Fookes, Clinton; Nguyen, Kien; Denman, Simon; Sridharan, Sridha; Dionisio, Sasha

2018-05-01

Semiology observation and characterization play a major role in the presurgical evaluation of epilepsy. However, the interpretation of patient movements has subjective and intrinsic challenges. In this paper, we develop approaches to attempt to automatically extract and classify semiological patterns from facial expressions. We address limitations of existing computer-based analytical approaches of epilepsy monitoring, where facial movements have largely been ignored. This is an area that has seen limited advances in the literature. Inspired by recent advances in deep learning, we propose two deep learning models, landmark-based and region-based, to quantitatively identify changes in facial semiology in patients with mesial temporal lobe epilepsy (MTLE) from spontaneous expressions during phase I monitoring. A dataset has been collected from the Mater Advanced Epilepsy Unit (Brisbane, Australia) and is used to evaluate our proposed approach. Our experiments show that a landmark-based approach achieves promising results in analyzing facial semiology, where movements can be effectively marked and tracked when there is a frontal face on visualization. However, the region-based counterpart with spatiotemporal features achieves more accurate results when confronted with extreme head positions. A multifold cross-validation of the region-based approach exhibited an average test accuracy of 95.19% and an average AUC of 0.98 of the ROC curve. Conversely, a leave-one-subject-out cross-validation scheme for the same approach reveals a reduction in accuracy for the model as it is affected by data limitations and achieves an average test accuracy of 50.85%. Overall, the proposed deep learning models have shown promise in quantifying ictal facial movements in patients with MTLE. In turn, this may serve to enhance the automated presurgical epilepsy evaluation by allowing for standardization, mitigating bias, and assessing key features. The computer-aided diagnosis may help to

Identification of facial shape by applying golden ratio to the facial measurements: an interracial study in malaysian population.

PubMed

Packiriswamy, Vasanthakumar; Kumar, Pramod; Rao, Mohandas

2012-12-01

The "golden ratio" is considered as a universal facial aesthetical standard. Researcher's opinion that deviation from golden ratio can result in development of facial abnormalities. This study was designed to study the facial morphology and to identify individuals with normal, short, and long face. We studied 300 Malaysian nationality subjects aged 18-28 years of Chinese, Indian, and Malay extraction. The parameters measured were physiognomical facial height and width of face, and physiognomical facial index was calculated. Face shape was classified based on golden ratio. Independent t test was done to test the difference between sexes and among the races. The mean values of the measurements and index showed significant sexual and interracial differences. Out of 300 subjects, the face shape was normal in 60 subjects, short in 224 subjects, and long in 16 subjects. As anticipated, the measurements showed variations according to gender and race. Only 60 subjects had a regular face shape, and remaining 240 subjects had irregular face shape (short and long). Since the short and long shape individuals may be at risk of developing various disorders, the knowledge of facial shapes in the given population is important for early diagnostic and treatment procedures.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Human listening studies reveal insights into object features extracted by echolocating dolphins

NASA Astrophysics Data System (ADS)

Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

A Modified Sparse Representation Method for Facial Expression Recognition.

PubMed

Wang, Wei; Xu, LiHong

2016-01-01

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

A Modified Sparse Representation Method for Facial Expression Recognition

PubMed Central

Wang, Wei; Xu, LiHong

2016-01-01

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result. PMID:26880878

Signatures of personality on dense 3D facial images.

PubMed

Hu, Sile; Xiong, Jieyi; Fu, Pengcheng; Qiao, Lu; Tan, Jingze; Jin, Li; Tang, Kun

2017-03-06

It has long been speculated that cues on the human face exist that allow observers to make reliable judgments of others' personality traits. However, direct evidence of association between facial shapes and personality is missing from the current literature. This study assessed the personality attributes of 834 Han Chinese volunteers (405 males and 429 females), utilising the five-factor personality model ('Big Five'), and collected their neutral 3D facial images. Dense anatomical correspondence was established across the 3D facial images in order to allow high-dimensional quantitative analyses of the facial phenotypes. In this paper, we developed a Partial Least Squares (PLS) -based method. We used composite partial least squares component (CPSLC) to test association between the self-tested personality scores and the dense 3D facial image data, then used principal component analysis (PCA) for further validation. Among the five personality factors, agreeableness and conscientiousness in males and extraversion in females were significantly associated with specific facial patterns. The personality-related facial patterns were extracted and their effects were extrapolated on simulated 3D facial models.

Functional connectivity between amygdala and facial regions involved in recognition of facial threat

PubMed Central

Harada, Tokiko; Ruffman, Ted; Sadato, Norihiro; Iidaka, Tetsuya

2013-01-01

The recognition of threatening faces is important for making social judgments. For example, threatening facial features of defendants could affect the decisions of jurors during a trial. Previous neuroimaging studies using faces of members of the general public have identified a pivotal role of the amygdala in perceiving threat. This functional magnetic resonance imaging study used face photographs of male prisoners who had been convicted of first-degree murder (MUR) as threatening facial stimuli. We compared the subjective ratings of MUR faces with those of control (CON) faces and examined how they were related to brain activation, particularly, the modulation of the functional connectivity between the amygdala and other brain regions. The MUR faces were perceived to be more threatening than the CON faces. The bilateral amygdala was shown to respond to both MUR and CON faces, but subtraction analysis revealed no significant difference between the two. Functional connectivity analysis indicated that the extent of connectivity between the left amygdala and the face-related regions (i.e. the superior temporal sulcus, inferior temporal gyrus and fusiform gyrus) was correlated with the subjective threat rating for the faces. We have demonstrated that the functional connectivity is modulated by vigilance for threatening facial features. PMID:22156740

Dermatological Feasibility of Multimodal Facial Color Imaging Modality for Cross-Evaluation of Facial Actinic Keratosis

PubMed Central

Bae, Youngwoo; Son, Taeyoon; Nelson, J. Stuart; Kim, Jae-Hong; Choi, Eung Ho; Jung, Byungjo

2010-01-01

Background/Purpose Digital color image analysis is currently considered as a routine procedure in dermatology. In our previous study, a multimodal facial color imaging modality (MFCIM), which provides a conventional, parallel- and cross-polarization, and fluorescent color image, was introduced for objective evaluation of various facial skin lesions. This study introduces a commercial version of MFCIM, DermaVision-PRO, for routine clinical use in dermatology and demonstrates its dermatological feasibility for cross-evaluation of skin lesions. Methods/Results Sample images of subjects with actinic keratosis or non-melanoma skin cancers were obtained at four different imaging modes. Various image analysis methods were applied to cross-evaluate the skin lesion and, finally, extract valuable diagnostic information. DermaVision-PRO is potentially a useful tool as an objective macroscopic imaging modality for quick prescreening and cross-evaluation of facial skin lesions. Conclusion DermaVision-PRO may be utilized as a useful tool for cross-evaluation of widely distributed facial skin lesions and an efficient database management of patient information. PMID:20923462

Facial Redness Increases Men's Perceived Healthiness and Attractiveness.

PubMed

Thorstenson, Christopher A; Pazda, Adam D; Elliot, Andrew J; Perrett, David I

2017-06-01

Past research has shown that peripheral and facial redness influences perceptions of attractiveness for men viewing women. The current research investigated whether a parallel effect is present when women rate men with varying facial redness. In four experiments, women judged the attractiveness of men's faces, which were presented with varying degrees of redness. We also examined perceived healthiness and other candidate variables as mediators of the red-attractiveness effect. The results show that facial redness positively influences ratings of men's attractiveness. Additionally, perceived healthiness was documented as a mediator of this effect, independent of other potential mediator variables. The current research emphasizes facial coloration as an important feature of social judgments.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Discrimination of emotional facial expressions by tufted capuchin monkeys (Sapajus apella).

PubMed

Calcutt, Sarah E; Rubin, Taylor L; Pokorny, Jennifer J; de Waal, Frans B M

2017-02-01

Tufted or brown capuchin monkeys (Sapajus apella) have been shown to recognize conspecific faces as well as categorize them according to group membership. Little is known, though, about their capacity to differentiate between emotionally charged facial expressions or whether facial expressions are processed as a collection of features or configurally (i.e., as a whole). In 3 experiments, we examined whether tufted capuchins (a) differentiate photographs of neutral faces from either affiliative or agonistic expressions, (b) use relevant facial features to make such choices or view the expression as a whole, and (c) demonstrate an inversion effect for facial expressions suggestive of configural processing. Using an oddity paradigm presented on a computer touchscreen, we collected data from 9 adult and subadult monkeys. Subjects discriminated between emotional and neutral expressions with an exceptionally high success rate, including differentiating open-mouth threats from neutral expressions even when the latter contained varying degrees of visible teeth and mouth opening. They also showed an inversion effect for facial expressions, results that may indicate that quickly recognizing expressions does not originate solely from feature-based processing but likely a combination of relational processes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector.

PubMed

Lei, Baiying; Tan, Ee-Leng; Chen, Siping; Zhuo, Liu; Li, Shengli; Ni, Dong; Wang, Tianfu

2015-01-01

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.

Greater perceptual sensitivity to happy facial expression.

PubMed

Maher, Stephen; Ekstrom, Tor; Chen, Yue

2014-01-01

Perception of subtle facial expressions is essential for social functioning; yet it is unclear if human perceptual sensitivities differ in detecting varying types of facial emotions. Evidence diverges as to whether salient negative versus positive emotions (such as sadness versus happiness) are preferentially processed. Here, we measured perceptual thresholds for the detection of four types of emotion in faces--happiness, fear, anger, and sadness--using psychophysical methods. We also evaluated the association of the perceptual performances with facial morphological changes between neutral and respective emotion types. Human observers were highly sensitive to happiness compared with the other emotional expressions. Further, this heightened perceptual sensitivity to happy expressions can be attributed largely to the emotion-induced morphological change of a particular facial feature (end-lip raise).

Extraction of edge-based and region-based features for object recognition

NASA Astrophysics Data System (ADS)

Coutts, Benjamin; Ravi, Srinivas; Hu, Gongzhu; Shrikhande, Neelima

1993-08-01

One of the central problems of computer vision is object recognition. A catalogue of model objects is described as a set of features such as edges and surfaces. The same features are extracted from the scene and matched against the models for object recognition. Edges and surfaces extracted from the scenes are often noisy and imperfect. In this paper algorithms are described for improving low level edge and surface features. Existing edge extraction algorithms are applied to the intensity image to obtain edge features. Initial edges are traced by following directions of the current contour. These are improved by using corresponding depth and intensity information for decision making at branch points. Surface fitting routines are applied to the range image to obtain planar surface patches. An algorithm of region growing is developed that starts with a coarse segmentation and uses quadric surface fitting to iteratively merge adjacent regions into quadric surfaces based on approximate orthogonal distance regression. Surface information obtained is returned to the edge extraction routine to detect and remove fake edges. This process repeats until no more merging or edge improvement can take place. Both synthetic (with Gaussian noise) and real images containing multiple object scenes have been tested using the merging criteria. Results appeared quite encouraging.

The algorithm of fast image stitching based on multi-feature extraction

NASA Astrophysics Data System (ADS)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Human Facial Shape and Size Heritability and Genetic Correlations.

PubMed

Cole, Joanne B; Manyama, Mange; Larson, Jacinda R; Liberton, Denise K; Ferrara, Tracey M; Riccardi, Sheri L; Li, Mao; Mio, Washington; Klein, Ophir D; Santorico, Stephanie A; Hallgrímsson, Benedikt; Spritz, Richard A

2017-02-01

The human face is an array of variable physical features that together make each of us unique and distinguishable. Striking familial facial similarities underscore a genetic component, but little is known of the genes that underlie facial shape differences. Numerous studies have estimated facial shape heritability using various methods. Here, we used advanced three-dimensional imaging technology and quantitative human genetics analysis to estimate narrow-sense heritability, heritability explained by common genetic variation, and pairwise genetic correlations of 38 measures of facial shape and size in normal African Bantu children from Tanzania. Specifically, we fit a linear mixed model of genetic relatedness between close and distant relatives to jointly estimate variance components that correspond to heritability explained by genome-wide common genetic variation and variance explained by uncaptured genetic variation, the sum representing total narrow-sense heritability. Our significant estimates for narrow-sense heritability of specific facial traits range from 28 to 67%, with horizontal measures being slightly more heritable than vertical or depth measures. Furthermore, for over half of facial traits, >90% of narrow-sense heritability can be explained by common genetic variation. We also find high absolute genetic correlation between most traits, indicating large overlap in underlying genetic loci. Not surprisingly, traits measured in the same physical orientation (i.e., both horizontal or both vertical) have high positive genetic correlations, whereas traits in opposite orientations have high negative correlations. The complex genetic architecture of facial shape informs our understanding of the intricate relationships among different facial features as well as overall facial development. Copyright © 2017 by the Genetics Society of America.

Integration of internal and external facial features in 8- to 10-year-old children and adults.

PubMed

Meinhardt-Injac, Bozana; Persike, Malte; Meinhardt, Günter

2014-06-01

Investigation of whole-part and composite effects in 4- to 6-year-old children gave rise to claims that face perception is fully mature within the first decade of life (Crookes & McKone, 2009). However, only internal features were tested, and the role of external features was not addressed, although external features are highly relevant for holistic face perception (Sinha & Poggio, 1996; Axelrod & Yovel, 2010, 2011). In this study, 8- to 10-year-old children and adults performed a same-different matching task with faces and watches. In this task participants attended to either internal or external features. Holistic face perception was tested using a congruency paradigm, in which face and non-face stimuli either agreed or disagreed in both features (congruent contexts) or just in the attended ones (incongruent contexts). In both age groups, pronounced context congruency and inversion effects were found for faces, but not for watches. These findings indicate holistic feature integration for faces. While inversion effects were highly similar in both age groups, context congruency effects were stronger for children. Moreover, children's face matching performance was generally better when attending to external compared to internal features. Adults tended to perform better when attending to internal features. Our results indicate that both adults and 8- to 10-year-old children integrate external and internal facial features into holistic face representations. However, in children's face representations external features are much more relevant. These findings suggest that face perception is holistic but still not adult-like at the end of the first decade of life. Copyright © 2014 Elsevier B.V. All rights reserved.

A Hybrid Neural Network and Feature Extraction Technique for Target Recognition.

DTIC Science & Technology

target features are extracted, the extracted data being evaluated in an artificial neural network to identify a target at a location within the image scene from which the different viewing angles extend.

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data

PubMed Central

Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

2016-01-01

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew’s Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions. PMID:26861308

Aberrant patterns of visual facial information usage in schizophrenia.

PubMed

Clark, Cameron M; Gosselin, Frédéric; Goghari, Vina M

2013-05-01

Deficits in facial emotion perception have been linked to poorer functional outcome in schizophrenia. However, the relationship between abnormal emotion perception and functional outcome remains poorly understood. To better understand the nature of facial emotion perception deficits in schizophrenia, we used the Bubbles Facial Emotion Perception Task to identify differences in usage of visual facial information in schizophrenia patients (n = 20) and controls (n = 20), when differentiating between angry and neutral facial expressions. As hypothesized, schizophrenia patients required more facial information than controls to accurately differentiate between angry and neutral facial expressions, and they relied on different facial features and spatial frequencies to differentiate these facial expressions. Specifically, schizophrenia patients underutilized the eye regions, overutilized the nose and mouth regions, and virtually ignored information presented at the lowest levels of spatial frequency. In addition, a post hoc one-tailed t test revealed a positive relationship of moderate strength between the degree of divergence from "normal" visual facial information usage in the eye region and lower overall social functioning. These findings provide direct support for aberrant patterns of visual facial information usage in schizophrenia in differentiating between socially salient emotional states. © 2013 American Psychological Association

Research on feature extraction techniques of Hainan Li brocade pattern

NASA Astrophysics Data System (ADS)

Zhou, Yuping; Chen, Fuqiang; Zhou, Yuhua

2016-03-01

Hainan Li brocade skills has been listed as world non-material cultural heritage preservation, therefore, the research on Hainan Li brocade patterns plays an important role in Li brocade culture inheritance. The meaning of Li brocade patterns was analyzed and the shape feature extraction techniques to original Li brocade patterns were advanced in this paper, based on the contour tracking algorithm. First, edge detection was made on the design patterns, and then the morphological closing operation was used to smooth the image, and finally contour tracking was used to extract the outer contours of Li brocade patterns. The extracted contour features were processed by means of morphology, and digital characteristics of contours are obtained by invariant moments. At last, different patterns of Li brocade design are briefly analyzed according to the digital characteristics. The results showed that the pattern extraction method to Li brocade pattern shapes is feasible and effective according to above method.

Looking like a criminal: stereotypical black facial features promote face source memory error.

PubMed

Kleider, Heather M; Cavrak, Sarah E; Knuycky, Leslie R

2012-11-01

The present studies tested whether African American face type (stereotypical or nonstereotypical) facilitated stereotype-consistent categorization, and whether that categorization influenced memory accuracy and errors. Previous studies have shown that stereotypically Black features are associated with crime and violence (e.g., Blair, Judd, & Chapleau Psychological Science 15:674-679, 2004; Blair, Judd, & Fallman Journal of Personality and Social Psychology 87:763-778, 2004; Blair, Judd, Sadler, & Jenkins Journal of Personality and Social Psychology 83:5-252002); here, we extended this finding to investigate whether there is a bias toward remembering and recategorizing stereotypical faces as criminals. Using category labels, consistent (or inconsistent) with race-based expectations, we tested whether face recognition and recategorization were driven by the similarity between a target's facial features and a stereotyped category (i.e., stereotypical Black faces associated with crime/violence). The results revealed that stereotypical faces were associated more often with a stereotype-consistent label (Study 1), were remembered and correctly recategorized as criminals (Studies 2-4), and were miscategorized as criminals when memory failed. These effects occurred regardless of race or gender. Together, these findings suggest that face types have strong category associations that can promote stereotype-motivated recognition errors. Implications for eyewitness accuracy are discussed.

Facial Attractiveness Assessment using Illustrated Questionnairers

PubMed Central

MESAROS, ANCA; CORNEA, DANIELA; CIOARA, LIVIU; DUDEA, DIANA; MESAROS, MICHAELA; BADEA, MINDRA

2015-01-01

Introduction. An attractive facial appearance is considered nowadays to be a decisive factor in establishing successful interactions between humans. In relation to this topic, scientific literature states that some of the facial features have more impact then others, and important authors revealed that certain proportions between different anthropometrical landmarks are mandatory for an attractive facial appearance. Aim. Our study aims to assess if certain facial features count differently in people’s opinion while assessing facial attractiveness in correlation with factors such as age, gender, specific training and culture. Material and methods. A 5-item multiple choice illustrated questionnaire was presented to 236 dental students. The Photoshop CS3 software was used in order to obtain the sets of images for the illustrated questions. The original image was handpicked from the internet by a panel of young dentists from a series of 15 pictures of people considered to have attractive faces. For each of the questions, the images presented were simulating deviations from the ideally symmetric and proportionate face. The sets of images consisted in multiple variations of deviations mixed with the original photo. Junior and sophomore year students from our dental medical school, having different nationalities were required to participate in our questionnaire. Simple descriptive statistics were used to interpret the data. Results. Assessing the results obtained from the questionnaire it was observed that a majority of students considered as unattractive the overdevelopment of the lower third, while the initial image with perfect symmetry and proportion was considered as the most attractive by only 38.9% of the subjects. Likewise, regarding the symmetry 36.86% considered unattractive the canting of the inter-commissural line. The interviewed subjects considered that for a face to be attractive it needs to have harmonious proportions between the different facial

A flexible data-driven comorbidity feature extraction framework.

PubMed

Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid

2016-06-01

Disease and symptom diagnostic codes are a valuable resource for classifying and predicting patient outcomes. In this paper, we propose a novel methodology for utilizing disease diagnostic information in a predictive machine learning framework. Our methodology relies on a novel, clustering-based feature extraction framework using disease diagnostic information. To reduce the data dimensionality, we identify disease clusters using co-occurrence statistics. We optimize the number of generated clusters in the training set and then utilize these clusters as features to predict patient severity of condition and patient readmission risk. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million hospital discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 Congestive Heart Failure (CHF) patients and the UCI 130-US diabetes dataset that includes admissions from 69,980 diabetic patients. We compare our cluster-based feature set with the commonly used comorbidity frameworks including Charlson's index, Elixhauser's comorbidities and their variations. The proposed approach was shown to have significant gains between 10.7-22.1% in predictive accuracy for CHF severity of condition prediction and 4.65-5.75% in diabetes readmission prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image feature extraction based on the camouflage effectiveness evaluation

NASA Astrophysics Data System (ADS)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

The impact of the stimulus features and task instructions on facial processing in social anxiety: an ERP investigation.

PubMed

Peschard, Virginie; Philippot, Pierre; Joassin, Frédéric; Rossignol, Mandy

2013-04-01

Social anxiety has been characterized by an attentional bias towards threatening faces. Electrophysiological studies have demonstrated modulations of cognitive processing from 100 ms after stimulus presentation. However, the impact of the stimulus features and task instructions on facial processing remains unclear. Event-related potentials were recorded while high and low socially anxious individuals performed an adapted Stroop paradigm that included a colour-naming task with non-emotional stimuli, an emotion-naming task (the explicit task) and a colour-naming task (the implicit task) on happy, angry and neutral faces. Whereas the impact of task factors was examined by contrasting an explicit and an implicit emotional task, the effects of perceptual changes on facial processing were explored by including upright and inverted faces. The findings showed an enhanced P1 in social anxiety during the three tasks, without a moderating effect of the type of task or stimulus. These results suggest a global modulation of attentional processing in performance situations. Copyright © 2013 Elsevier B.V. All rights reserved.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Sensorineural deafness, distinctive facial features, and abnormal cranial bones: a new variant of Waardenburg syndrome?

PubMed

Gad, Alona; Laurino, Mercy; Maravilla, Kenneth R; Matsushita, Mark; Raskind, Wendy H

2008-07-15

The Waardenburg syndromes (WS) account for approximately 2% of congenital sensorineural deafness. This heterogeneous group of diseases currently can be categorized into four major subtypes (WS types 1-4) on the basis of characteristic clinical features. Multiple genes have been implicated in WS, and mutations in some genes can cause more than one WS subtype. In addition to eye, hair, and skin pigmentary abnormalities, dystopia canthorum and broad nasal bridge are seen in WS type 1. Mutations in the PAX3 gene are responsible for the condition in the majority of these patients. In addition, mutations in PAX3 have been found in WS type 3 that is distinguished by musculoskeletal abnormalities, and in a family with a rare subtype of WS, craniofacial-deafness-hand syndrome (CDHS), characterized by dysmorphic facial features, hand abnormalities, and absent or hypoplastic nasal and wrist bones. Here we describe a woman who shares some, but not all features of WS type 3 and CDHS, and who also has abnormal cranial bones. All sinuses were hypoplastic, and the cochlea were small. No sequence alteration in PAX3 was found. These observations broaden the clinical range of WS and suggest there may be genetic heterogeneity even within the CDHS subtype. 2008 Wiley-Liss, Inc.

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Facial Expression Presentation for Real-Time Internet Communication

NASA Astrophysics Data System (ADS)

Dugarry, Alexandre; Berrada, Aida; Fu, Shan

2003-01-01

Text, voice and video images are the most common forms of media content for instant communication on the Internet. Studies have shown that facial expressions convey much richer information than text and voice during a face-to-face conversation. The currently available real time means of communication (instant text messages, chat programs and videoconferencing), however, have major drawbacks in terms of exchanging facial expression. The first two means do not involve the image transmission, whilst video conferencing requires a large bandwidth that is not always available, and the transmitted image sequence is neither smooth nor without delay. The objective of the work presented here is to develop a technique that overcomes these limitations, by extracting the facial expression of speakers and to realise real-time communication. In order to get the facial expressions, the main characteristics of the image are emphasized. Interpolation is performed on edge points previously detected to create geometric shapes such as arcs, lines, etc. The regional dominant colours of the pictures are also extracted and the combined results are subsequently converted into Scalable Vector Graphics (SVG) format. The application based on the proposed technique aims at being used simultaneously with chat programs and being able to run on any platform.

Identification of Facial Shape by Applying Golden Ratio to the Facial Measurements: An Interracial Study in Malaysian Population

PubMed Central

Packiriswamy, Vasanthakumar; Kumar, Pramod; Rao, Mohandas

2012-01-01

Background: The “golden ratio” is considered as a universal facial aesthetical standard. Researcher's opinion that deviation from golden ratio can result in development of facial abnormalities. Aims: This study was designed to study the facial morphology and to identify individuals with normal, short, and long face. Materials and Methods: We studied 300 Malaysian nationality subjects aged 18-28 years of Chinese, Indian, and Malay extraction. The parameters measured were physiognomical facial height and width of face, and physiognomical facial index was calculated. Face shape was classified based on golden ratio. Independent t test was done to test the difference between sexes and among the races. Results: The mean values of the measurements and index showed significant sexual and interracial differences. Out of 300 subjects, the face shape was normal in 60 subjects, short in 224 subjects, and long in 16 subjects. Conclusion: As anticipated, the measurements showed variations according to gender and race. Only 60 subjects had a regular face shape, and remaining 240 subjects had irregular face shape (short and long). Since the short and long shape individuals may be at risk of developing various disorders, the knowledge of facial shapes in the given population is important for early diagnostic and treatment procedures. PMID:23272303

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Thermal feature extraction of servers in a datacenter using thermal image registration

NASA Astrophysics Data System (ADS)

Liu, Hang; Ran, Jian; Xie, Ting; Gao, Shan

2017-09-01

Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.

Evidence of a Shift from Featural to Configural Face Processing in Infancy

ERIC Educational Resources Information Center

Schwarzer, Gudrun; Zauner, Nicola; Jovanovic, Bianca

2007-01-01

Two experiments examined whether 4-, 6-, and 10-month-old infants process natural looking faces by feature, i.e. processing internal facial features independently of the facial context or holistically by processing the features in conjunction with the facial context. Infants were habituated to two faces and looking time was measured. After…

A method for automatic feature points extraction of human vertebrae three-dimensional model

NASA Astrophysics Data System (ADS)

Wu, Zhen; Wu, Junsheng

2017-05-01

A method for automatic extraction of the feature points of the human vertebrae three-dimensional model is presented. Firstly, the statistical model of vertebrae feature points is established based on the results of manual vertebrae feature points extraction. Then anatomical axial analysis of the vertebrae model is performed according to the physiological and morphological characteristics of the vertebrae. Using the axial information obtained from the analysis, a projection relationship between the statistical model and the vertebrae model to be extracted is established. According to the projection relationship, the statistical model is matched with the vertebrae model to get the estimated position of the feature point. Finally, by analyzing the curvature in the spherical neighborhood with the estimated position of feature points, the final position of the feature points is obtained. According to the benchmark result on multiple test models, the mean relative errors of feature point positions are less than 5.98%. At more than half of the positions, the error rate is less than 3% and the minimum mean relative error is 0.19%, which verifies the effectiveness of the method.

Facial Scar Revision: Understanding Facial Scar Treatment

MedlinePlus

... Contact Us Trust your face to a facial plastic surgeon Facial Scar Revision Understanding Facial Scar Treatment ... face like the eyes or lips. A facial plastic surgeon has many options for treating and improving ...

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

PubMed Central

Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

2009-01-01

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

Feature extraction using convolutional neural network for classifying breast density in mammographic images

NASA Astrophysics Data System (ADS)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

A quick eye to anger: An investigation of a differential effect of facial features in detecting angry and happy expressions.

PubMed

Lo, L Y; Cheng, M Y

2017-06-01

Detection of angry and happy faces is generally found to be easier and faster than that of faces expressing emotions other than anger or happiness. This can be explained by the threatening account and the feature account. Few empirical studies have explored the interaction between these two accounts which are seemingly, but not necessarily, mutually exclusive. The present studies hypothesised that prominent facial features are important in facilitating the detection process of both angry and happy expressions; yet the detection of happy faces was more facilitated by the prominent features than angry faces. Results confirmed the hypotheses and indicated that participants reacted faster to the emotional expressions with prominent features (in Study 1) and the detection of happy faces was more facilitated by the prominent feature than angry faces (in Study 2). The findings are compatible with evolutionary speculation which suggests that the angry expression is an alarming signal of potential threats to survival. Compared to the angry faces, the happy faces need more salient physical features to obtain a similar level of processing efficiency. © 2015 International Union of Psychological Science.

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

NASA Astrophysics Data System (ADS)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Facial Contrast Is a Cross-Cultural Cue for Perceiving Age

PubMed Central

Porcheron, Aurélie; Mauger, Emmanuelle; Soppelsa, Frédérique; Liu, Yuli; Ge, Liezhong; Pascalis, Olivier; Russell, Richard; Morizot, Frédérique

2017-01-01

Age is a fundamental social dimension and a youthful appearance is of importance for many individuals, perhaps because it is a relevant predictor of aspects of health, facial attractiveness and general well-being. We recently showed that facial contrast—the color and luminance difference between facial features and the surrounding skin—is age-related and a cue to age perception of Caucasian women. Specifically, aspects of facial contrast decrease with age in Caucasian women, and Caucasian female faces with higher contrast look younger (Porcheron et al., 2013). Here we investigated faces of other ethnic groups and raters of other cultures to see whether facial contrast is a cross-cultural youth-related attribute. Using large sets of full face color photographs of Chinese, Latin American and black South African women aged 20–80, we measured the luminance and color contrast between the facial features (the eyes, the lips, and the brows) and the surrounding skin. Most aspects of facial contrast that were previously found to decrease with age in Caucasian women were also found to decrease with age in the other ethnic groups. Though the overall pattern of changes with age was common to all women, there were also some differences between the groups. In a separate study, individual faces of the 4 ethnic groups were perceived younger by French and Chinese participants when the aspects of facial contrast that vary with age in the majority of faces were artificially increased, but older when they were artificially decreased. Altogether these findings indicate that facial contrast is a cross-cultural cue to youthfulness. Because cosmetics were shown to enhance facial contrast, this work provides some support for the notion that a universal function of cosmetics is to make female faces look younger. PMID:28790941

Facial Contrast Is a Cross-Cultural Cue for Perceiving Age.

PubMed

Porcheron, Aurélie; Mauger, Emmanuelle; Soppelsa, Frédérique; Liu, Yuli; Ge, Liezhong; Pascalis, Olivier; Russell, Richard; Morizot, Frédérique

2017-01-01

Age is a fundamental social dimension and a youthful appearance is of importance for many individuals, perhaps because it is a relevant predictor of aspects of health, facial attractiveness and general well-being. We recently showed that facial contrast-the color and luminance difference between facial features and the surrounding skin-is age-related and a cue to age perception of Caucasian women. Specifically, aspects of facial contrast decrease with age in Caucasian women, and Caucasian female faces with higher contrast look younger (Porcheron et al., 2013). Here we investigated faces of other ethnic groups and raters of other cultures to see whether facial contrast is a cross-cultural youth-related attribute. Using large sets of full face color photographs of Chinese, Latin American and black South African women aged 20-80, we measured the luminance and color contrast between the facial features (the eyes, the lips, and the brows) and the surrounding skin. Most aspects of facial contrast that were previously found to decrease with age in Caucasian women were also found to decrease with age in the other ethnic groups. Though the overall pattern of changes with age was common to all women, there were also some differences between the groups. In a separate study, individual faces of the 4 ethnic groups were perceived younger by French and Chinese participants when the aspects of facial contrast that vary with age in the majority of faces were artificially increased, but older when they were artificially decreased. Altogether these findings indicate that facial contrast is a cross-cultural cue to youthfulness. Because cosmetics were shown to enhance facial contrast, this work provides some support for the notion that a universal function of cosmetics is to make female faces look younger.

Linearly Supporting Feature Extraction for Automated Estimation of Stellar Atmospheric Parameters

NASA Astrophysics Data System (ADS)

Li, Xiangru; Lu, Yu; Comte, Georges; Luo, Ali; Zhao, Yongheng; Wang, Yongjun

2015-05-01

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H]. “Linearly supporting” means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)bs; third, estimate the atmospheric parameters {{T}{\\tt{eff} }}, log g, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both the wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate {{T}{\\tt{eff} }}, 62 features for log g, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Parameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log {{T}{\\tt{eff} }} (83 K for {{T}{\\tt{eff} }}), 0.2345 dex for log g, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log {{T}{\\tt{eff} }} (32 K for {{T}{\\tt{eff} }}), 0.0337 dex for log g, and 0.0268 dex for [Fe/H].

Facial Expression Influences Face Identity Recognition During the Attentional Blink

PubMed Central

2014-01-01

Emotional stimuli (e.g., negative facial expressions) enjoy prioritized memory access when task relevant, consistent with their ability to capture attention. Whether emotional expression also impacts on memory access when task-irrelevant is important for arbitrating between feature-based and object-based attentional capture. Here, the authors address this question in 3 experiments using an attentional blink task with face photographs as first and second target (T1, T2). They demonstrate reduced neutral T2 identity recognition after angry or happy T1 expression, compared to neutral T1, and this supports attentional capture by a task-irrelevant feature. Crucially, after neutral T1, T2 identity recognition was enhanced and not suppressed when T2 was angry—suggesting that attentional capture by this task-irrelevant feature may be object-based and not feature-based. As an unexpected finding, both angry and happy facial expressions suppress memory access for competing objects, but only angry facial expression enjoyed privileged memory access. This could imply that these 2 processes are relatively independent from one another. PMID:25286076

Facial expression influences face identity recognition during the attentional blink.

PubMed

Bach, Dominik R; Schmidt-Daffy, Martin; Dolan, Raymond J

2014-12-01

Emotional stimuli (e.g., negative facial expressions) enjoy prioritized memory access when task relevant, consistent with their ability to capture attention. Whether emotional expression also impacts on memory access when task-irrelevant is important for arbitrating between feature-based and object-based attentional capture. Here, the authors address this question in 3 experiments using an attentional blink task with face photographs as first and second target (T1, T2). They demonstrate reduced neutral T2 identity recognition after angry or happy T1 expression, compared to neutral T1, and this supports attentional capture by a task-irrelevant feature. Crucially, after neutral T1, T2 identity recognition was enhanced and not suppressed when T2 was angry-suggesting that attentional capture by this task-irrelevant feature may be object-based and not feature-based. As an unexpected finding, both angry and happy facial expressions suppress memory access for competing objects, but only angry facial expression enjoyed privileged memory access. This could imply that these 2 processes are relatively independent from one another.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Features extraction algorithm about typical railway perimeter intrusion event

NASA Astrophysics Data System (ADS)

Zhou, Jieyun; Wang, Chaodong; Liu, Lihai

2017-10-01

Research purposes: Optical fiber vibration sensing system has been widely used in the oil, gas, frontier defence, prison and power industries. But, there are few reports about the application in railway defence. That is because the surrounding environment is complicated and there are many challenges to be overcomed in the optical fiber vibration sensing system application. For example, how to eliminate the effects of vibration caused by train, the natural environments such as wind and rain and how to identify and classify the intrusion events. In order to solve these problems, the feature signals of these events should be extracted firstly. Research conclusions: (1) In optical fiber vibration sensing system based on Sagnac interferometer, the peak-to-peak value, peak-to-average ratio, standard deviation, zero-crossing rate, short-term energy and kurtosis may serve as feature signals. (2) The feature signals of resting state, climbing concrete fence, breaking barbed wire, knocking concrete fence and rainstorm have been extracted, which shows significant difference among each other. (3) The research conclusions can be used in the identification and classification of intrusion events.

Facial Structure Predicts Sexual Orientation in Both Men and Women.

PubMed

Skorska, Malvina N; Geniole, Shawn N; Vrysen, Brandon M; McCormick, Cheryl M; Bogaert, Anthony F

2015-07-01

Biological models have typically framed sexual orientation in terms of effects of variation in fetal androgen signaling on sexual differentiation, although other biological models exist. Despite marked sex differences in facial structure, the relationship between sexual orientation and facial structure is understudied. A total of 52 lesbian women, 134 heterosexual women, 77 gay men, and 127 heterosexual men were recruited at a Canadian campus and various Canadian Pride and sexuality events. We found that facial structure differed depending on sexual orientation; substantial variation in sexual orientation was predicted using facial metrics computed by a facial modelling program from photographs of White faces. At the univariate level, lesbian and heterosexual women differed in 17 facial features (out of 63) and four were unique multivariate predictors in logistic regression. Gay and heterosexual men differed in 11 facial features at the univariate level, of which three were unique multivariate predictors. Some, but not all, of the facial metrics differed between the sexes. Lesbian women had noses that were more turned up (also more turned up in heterosexual men), mouths that were more puckered, smaller foreheads, and marginally more masculine face shapes (also in heterosexual men) than heterosexual women. Gay men had more convex cheeks, shorter noses (also in heterosexual women), and foreheads that were more tilted back relative to heterosexual men. Principal components analysis and discriminant functions analysis generally corroborated these results. The mechanisms underlying variation in craniofacial structure--both related and unrelated to sexual differentiation--may thus be important in understanding the development of sexual orientation.

Validation of image analysis techniques to measure skin aging features from facial photographs.

PubMed

Hamer, M A; Jacobs, L C; Lall, J S; Wollstein, A; Hollestein, L M; Rae, A R; Gossage, K W; Hofman, A; Liu, F; Kayser, M; Nijsten, T; Gunn, D A

2015-11-01

Accurate measurement of the extent skin has aged is crucial for skin aging research. Image analysis offers a quick and consistent approach for quantifying skin aging features from photographs, but is prone to technical bias and requires proper validation. Facial photographs of 75 male and 75 female North-European participants, randomly selected from the Rotterdam Study, were graded by two physicians using photonumeric scales for wrinkles (full face, forehead, crow's feet, nasolabial fold and upper lip), pigmented spots and telangiectasia. Image analysis measurements of the same features were optimized using photonumeric grades from 50 participants, then compared to photonumeric grading in the 100 remaining participants stratified by sex. The inter-rater reliability of the photonumeric grades was good to excellent (intraclass correlation coefficients 0.65-0.93). Correlations between the digital measures and the photonumeric grading were moderate to excellent for all the wrinkle comparisons (Spearman's rho ρ = 0.52-0.89) bar the upper lip wrinkles in the men (fair, ρ = 0.30). Correlations were moderate to good for pigmented spots and telangiectasia (ρ = 0.60-0.75). These comparisons demonstrate that all the image analysis measures, bar the upper lip measure in the men, are suitable for use in skin aging research and highlight areas of improvement for future refinements of the techniques. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons.

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 +/- 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

Proposal of Self-Learning and Recognition System of Facial Expression

NASA Astrophysics Data System (ADS)

Ogawa, Yukihiro; Kato, Kunihito; Yamamoto, Kazuhiko

We describe realization of more complicated function by using the information acquired from some equipped unripe functions. The self-learning and recognition system of the human facial expression, which achieved under the natural relation between human and robot, are proposed. The robot with this system can understand human facial expressions and behave according to their facial expressions after the completion of learning process. The system modelled after the process that a baby learns his/her parents’ facial expressions. Equipping the robot with a camera the system can get face images and equipping the CdS sensors on the robot’s head the robot can get the information of human action. Using the information of these sensors, the robot can get feature of each facial expression. After self-learning is completed, when a person changed his facial expression in front of the robot, the robot operates actions under the relevant facial expression.

Ethnic and Gender Considerations in the Use of Facial Injectables: Asian Patients.

PubMed

Liew, Steven

2015-11-01

Asians have distinct facial characteristics due to underlying skeletal and morphological features that differ greatly with those of whites. This together with the higher sun protection factor and the differences in the quality of the skin and soft tissue create a profound effect on their aging process. Understanding of these differences and their effects in the aging process in Asians is crucial in determining effective utilization and placement of injectable products to ensure optimal aesthetic outcomes. For younger Asian women, the main treatment goal is to address the inherent structural deficits through reshaping and the provision of facial support. Facial injectables are used to provide anterior projection, to reduce facial width, and to lengthen facial height. In the older group, the aim is for rejuvenation and also to address the underlying structural issues that has compounded due to age-related volume loss. Asian women requesting cosmetic procedures do not want to be Westernized but rather seeking to enhance and optimize their Asian ethnic features.

Chemical-induced disease relation extraction with various linguistic features.

PubMed

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders.

PubMed

Valentine, Matthew; Bihm, Dustin C J; Wolf, Lior; Hoyme, H Eugene; May, Philip A; Buckley, David; Kalberg, Wendy; Abdul-Rahman, Omar A

2017-12-01

To compare the detection of facial attributes by computer-based facial recognition software of 2-D images against standard, manual examination in fetal alcohol spectrum disorders (FASD). Participants were gathered from the Fetal Alcohol Syndrome Epidemiology Research database. Standard frontal and oblique photographs of children were obtained during a manual, in-person dysmorphology assessment. Images were submitted for facial analysis conducted by the facial dysmorphology novel analysis technology (an automated system), which assesses ratios of measurements between various facial landmarks to determine the presence of dysmorphic features. Manual blinded dysmorphology assessments were compared with those obtained via the computer-aided system. Areas under the curve values for individual receiver-operating characteristic curves revealed the computer-aided system (0.88 ± 0.02) to be comparable to the manual method (0.86 ± 0.03) in detecting patients with FASD. Interestingly, cases of alcohol-related neurodevelopmental disorder (ARND) were identified more efficiently by the computer-aided system (0.84 ± 0.07) in comparison to the manual method (0.74 ± 0.04). A facial gestalt analysis of patients with ARND also identified more generalized facial findings compared to the cardinal facial features seen in more severe forms of FASD. We found there was an increased diagnostic accuracy for ARND via our computer-aided method. As this category has been historically difficult to diagnose, we believe our experiment demonstrates that facial dysmorphology novel analysis technology can potentially improve ARND diagnosis by introducing a standardized metric for recognizing FASD-associated facial anomalies. Earlier recognition of these patients will lead to earlier intervention with improved patient outcomes. Copyright © 2017 by the American Academy of Pediatrics.

SD-MSAEs: Promoter recognition in human genome based on deep feature extraction.

PubMed

Xu, Wenxuan; Zhang, Li; Lu, Yaping

2016-06-01

The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAEs. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity. Copyright © 2016 Elsevier Inc. All rights reserved.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.