Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA = Gabor for Expression Recognition
PCA = Gabor for Expression Recognition
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Simultaneous Facial Action Tracking and Expression Recognition Using a Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributing expressional faces in 2-D emotional space
Proceedings of the 6th ACM international conference on Image and video retrieval
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The validity of PAD (Pleasure-Arousal-Dominance) theory in vision area and the feasibilityT on PAD based models for facial expression analysis are discussed in this paper. Three new models based on PAD theory are proposed and their feasibility is verified by experiments on Cohn-Kanade dataset and PAD dataset which is collected from well-designed psychological experiments. After combining Gabor feature and SVM (Support Vector Machine), the result can be further improved. Compared with the basic expression models, our experiments show that the predominance of PAD based model is that it can represent almost any states of expression. Finally, our preliminary experiments show that distinguishing different grades of the same expression is promising by our models.