Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
The emotional hearing aid: an assistive tool for children with Asperger syndrome
Universal Access in the Information Society
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic temporal segment detection and affect recognition from face and body display
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A psychologically-inspired match-score fusion mode for video-based facial expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Facial expression recognition using constructive feedforward neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AVEC 2012: the continuous audio/visual emotion challenge - an introduction
Proceedings of the 14th ACM international conference on Multimodal interaction
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Facial emotion recognition, the inference of an emotion from apparent facial expressions, in unconstrained settings is a typical case where algorithms perform poorly. A property of the AVEC2012 data set is that individuals in testing data are not encountered in training data. In these situations, conventional approaches suffer because models developed from training data cannot properly discriminate unforeseen testing samples. Additional information beyond the feature vectors is required for successful detection of emotions. We propose two similarity metrics that address the problems of a conventional approach: neutral similarity, measuring the intensity of an expression; and temporal similarity, measuring changes in an expression over time. These similarities are taken to be the energy of facial expressions, measured with a SIFT-based warping process. Our method improves correlation by 35.5% over the baseline approach on the frame-level sub-challenge.