PAD Model Based Facial Expression Analysis

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.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Wenyuan Bi,et al.  Distributing expressional faces in 2-D emotional space , 2007, CIVR '07.

[3]  A. Mehrabian.  Framework for a comprehensive description and measurement of emotional states. , 1995, Genetic, social, and general psychology monographs.

[4]  P. Ekman,et al.  Constants across cultures in the face and emotion. , 1971, Journal of personality and social psychology.

[5]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[7]  Franck Davoine,et al.  Facial expression recognition and synthesis based on an appearance model , 2004, Signal Process. Image Commun..

[8]  Gwen Littlewort,et al.  Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[9]  Shaogang Gong,et al.  Robust facial expression recognition using local binary patterns , 2005, IEEE International Conference on Image Processing 2005.

[10]  Garrison W. Cottrell,et al.  PCA = Gabor for Expression Recognition , 1999 .

[11]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  A. Mehrabian.  Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament , 1996 .

[13]  Fadi Dornaika,et al.  Simultaneous facial action tracking and expression recognition using a particle filter , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  A. Mehrabian.  Comparison of the PAD and PANAS as models for describing emotions and for differentiating anxiety from depression , 1997 .