Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Model and Process Architecture for Facial Expression Recognition
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
International Journal of Human-Computer Studies
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Emotional intensity-based facial expression cloning for low polygonal applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Emotion detection via discriminative kernel method
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Visual affect recognition
Interactive analysis and synthesis of facial expressions based on personal facial expression space
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Information Processing and Management: an International Journal
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Computer recognition of facial expressions of emotion
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A novel LDA and HMM-Based technique for emotion recognition from facial expressions
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
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The purpose of this study is not only to recognize some kind of facial expressions which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recognition can be achieved by extracting a variation from expressionless face with considering face area as a whole pattern. For the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. Using a elastic net model, a variation of facial expression is represented as motion vectors of the deformed Net from a facial edge image. Then, applying K-L expansion, the change of facial expression represented as the motion vectors of nodes is mapped into low dimensional eigen space, and estimation is achieved by projecting input images on to the Emotion Space. In this paper we have constructed three kinds of expression models: happiness, anger, surprise, and experimental results are evaluated.