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Research Interests: Machine Learning, Face Recognition, Support Vector Machines, Automatic Control, Gesture Recognition, and 13 moreMonte Carlo Methods, Parameter estimation, Speech, Geometry, Hidden Markov Models, Spontaneous speech, hidden Markov model, Image recognition, Support vector machine, Perforation, Markov chain monte carlo methods, Front end, and Facial Expression Recognition
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We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how continuous, spontaneous speech differs from isolated... more
We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how continuous, spontaneous speech differs from isolated words produced on command. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since people often nod or turn their heads, automatic recognition of spontaneous facial behavior requires methods for handling out-of-image-plane head rotations. Here we explore an approach based on 3-D warping of images into canonical views. We evaluated the performance of the approach as a front-end for a spontaneous expression recognition system using support vector machines and hidden Markov models. This system employed general purpose learning mechanisms that can be applied to recognition of any facial movement. The system was tested for recognition ...
this paper we show preliminary results for I. Recognition of posed facial actions in controlled conditions, and II. Recognition of spontaneous facial actions in freely behaving subjects
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Research Interests: Artificial Intelligence, Image Processing, Machine Learning, Face Recognition, Statistical Analysis, and 16 moreSupport Vector Machines, Facial expression, Feature Selection, Discriminant Analysis, Supervised Learning, Classification, Support vector machine, Real Time, Facies, Perforation, Filter Bank, Facial Action Coding System, Classifier, Facial Expression Recognition, Gabor Filter, and Action Unit
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Neuropsychological and neuroimaging evidence suggests that the human brain contains facial expression recognition detectors specialized for specific discrete emotions. However, some human behavioral data suggest that humans recognize... more
Neuropsychological and neuroimaging evidence suggests that the human brain contains facial expression recognition detectors specialized for specific discrete emotions. However, some human behavioral data suggest that humans recognize expressions as similar and not discrete entities. This latter observation has been taken to indicate that internal representations of facial expressions may be best characterized as varying along continuous underlying dimensions. To examine the potential compatibility of these two views, the present study compared human and support vector machine (SVM) facial expression recognition performance. Separate SVMs were trained to develop fully automatic optimal recognition of one of six basic emotional expressions in real-time with no explicit training on expression similarity. Performance revealed high recognition accuracy for expression prototypes. Without explicit training of similarity detection, magnitude of activation across each emotion-specific SVM captured human judgments of expression similarity. This evidence suggests that combinations of expert classifiers from separate internal neural representations result in similarity judgments between expressions, supporting the appearance of a continuous underlying dimensionality. Further, these data suggest similarity in expression meaning is supported by superficial similarities in expression appearance.
Research Interests: Psychology, Cognitive Science, Artificial Intelligence, Face recognition (Psychology), Facial expression, and 13 moreEmotions, Humans, Computer Simulation, Computer Model, Human behavior, Support vector machine, Human Brain, Real Time, Emotional Expression, Neuropsychologia, Facial Expression Recognition, Neurosciences, and Visual Features
ABSTRACT Engineered features have been heavily employed in computer vision. Recently, feature learning from unlabeled data for improving the performance of a given vision task has received increasing attention in both machine learning and... more
ABSTRACT Engineered features have been heavily employed in computer vision. Recently, feature learning from unlabeled data for improving the performance of a given vision task has received increasing attention in both machine learning and computer vision. In this paper, we present using unlabeled video data to learn spatiotemporal features for video classification tasks. Specifically, we employ independent component analysis (ICA) to learn spatiotemporal filters from natural videos, and then construct feature representations for the input videos in classification tasks based on the learned filters. We test the performance of proposed feature learning method with application to facial expression recognition. The experimental results on the well-known Cohn-Kanade database show that the learned features perform better than engineered features. The comparison experiments on recognition of low intensity expressions show that our method yields a better performance than spatiotemporal Gabor features.
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The goal of this special issue is to provide a state-of-the-art overview of new paradigms, methods, and challenges in Automatic Face and Gesture Recognition. As such we have made a selection of the best evaluated papers in The Eighth... more
The goal of this special issue is to provide a state-of-the-art overview of new paradigms, methods, and challenges in Automatic Face and Gesture Recognition. As such we have made a selection of the best evaluated papers in The Eighth International Conference on Automatic Face and Gesture Recognition 2008 and we have asked the authors to extend their articles so that they reflect not only the in-depth technical details but also new insights and thorough evaluations of the presented algorithms. Our aim was to highlight the hot ...
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Research Interests: Information Systems, Algorithms, Artificial Intelligence, Computer Vision, Machine Learning, and 12 moreSmiling, Face, Gesture Recognition, Biometry, Facial expression, Humans, Computer Simulation, Image Enhancement, Reproducibility of Results, Sensitivity and Specificity, Facial Expression Recognition, and Electrical And Electronic Engineering
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Charles Darwin (1872/1998) was the first to fully recognize that facial expression is one of the most powerful and immediate means for human beings to communicate their emotions, intentions, and opinions to each other. In addition to... more
Charles Darwin (1872/1998) was the first to fully recognize that facial expression is one of the most powerful and immediate means for human beings to communicate their emotions, intentions, and opinions to each other. In addition to providing information about affective ...
We investigate the problem of computer recognition of "Duchenne" vs. non-Duchenne smiles. Duchenne smiles include the contraction of the orbicularis oculi, the sphincter muscles that circle the eyes. Genuine, happy smiles can be... more
We investigate the problem of computer recognition of "Duchenne" vs. non-Duchenne smiles. Duchenne smiles include the contraction of the orbicularis oculi, the sphincter muscles that circle the eyes. Genuine, happy smiles can be di#erentiated from posed, or social ...
We present ongoing work on a project for automatic recognition of spon- taneous facial actions. Spontaneous facial expressions differ substan- tially from posed expressions, similar to how continuous, spontaneous speech differs from... more
We present ongoing work on a project for automatic recognition of spon- taneous facial actions. Spontaneous facial expressions differ substan- tially from posed expressions, similar to how continuous, spontaneous speech differs from isolated words produced on command. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects delib- erately faced the camera.
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ABSTRACT We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the Facial Action Coding System (FACS), as... more
ABSTRACT We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the Facial Action Coding System (FACS), as well as recognition of full facial expressions. Each video-frame is first scanned in real-time to detect approximately upright-frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training Support Vector Machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for recognition of full facial expressions in a 7-way forced choice was 93% correct, the best performance reported so far on the Cohn-Kanade FACS-coded expression dataset. We also applied the system to fully automated facial action coding. The present system classifies 18 action units, whether they occur singly or in combination with other actions, with a mean agreement rate of 94.5% with human FACS codes in the Cohn-Kanade dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics.
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Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a... more
Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a time scale of less than a second. In this paper we present progress on a perceptual primitive to automatically detect frontal faces in the video stream and code them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of ...