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Static topographic modeling for facial expression recognition and analysis
Computer Vision and Image Understanding
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Recognition Letters
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Personal and Ubiquitous Computing
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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Image and Vision Computing
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Image and Vision Computing
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Pattern Recognition
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HBU'10 Proceedings of the First international conference on Human behavior understanding
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Computer Vision and Image Understanding
Robust classification of face and head gestures in video
Image and Vision Computing
Engineering Applications of Artificial Intelligence
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PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Recognition of facial expressions by cortical multi-scale line and edge coding
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Facial expression recognition using geometric and appearance features
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Facial expression recognition using 3D facial feature distances
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A dynamic approach for detecting naturalistic affective states from facial videos during HCI
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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International Journal of Human-Computer Studies
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Image and Vision Computing
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Proceedings of the 15th ACM on International conference on multimodal interaction
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Pattern Recognition
Linear subspaces for facial expression recognition
Image Communication
Spontaneous facial expression recognition: A robust metric learning approach
Pattern Recognition
Facial expression recognition based on anatomy
Computer Vision and Image Understanding
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Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color face images. A multidetector approach to facial feature localization is utilized to spatially sample the profile contour and the contours of the facial components such as the eyes and the mouth. From the extracted contours of the facial features, we extract ten profile-contour fiducial points and 19 fiducial points of the contours of the facial components. Based on these, 32 individual facial muscle actions (AUs) occurring alone or in combination are recognized using rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.