Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Emotion Recognition Using a Cauchy Naive Bayes Classifier
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Bimodal HCI-related affect recognition
Proceedings of the 6th international conference on Multimodal interfaces
Subtle facial expression recognition using motion magnification
Pattern Recognition Letters
Facial expression recognition based on fusion of sparse representation
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
An embedded system for real-time facial expression recognition based on the extension theory
Computers & Mathematics with Applications
IEEE Transactions on Affective Computing
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Recognition of 3D facial expression dynamics
Image and Vision Computing
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Recognizing subtle emotional expression of human is a challenging and interesting problem in the field of human computer interaction. Multimodality is a prospective way to help solve this problem. Therefore, in this paper, we first take advantage of a novel "sparse representation" approach to compute the matching degree of current facial expression to each basic emotion class. Concurrently, we also use an eye tracker to obtain the instant pupillary response, which gives us clues to the subtle emotion. We combine the results of facial expression and pupillary information, take into account the previous emotional state to classify the current subtle emotional expression. Finally, a Markov Model is used to compute a directed graph to model the changes of human's emotion. The experimental results show that: First, the sparse representation has a good classification rate on facial expression; Second, the fusion of facial expression, pupillary size and previous emotional state is a promising strategy for analyzing subtle expression.