Motion field histograms for robust modeling of facial expressions

This paper presents motion field histograms as a new way of extracting facial features and modeling expressions. Features are based on local receptive field histograms, which are robust against errors in rotation, translation and scale changes during image alignment. Motion information is incorporated into the histograms by using difference images instead of raw images. We take the principal components of these histograms of selected facial regions and use the top 20 eigenvectors for compact representation. The eigen-coefficients are then used to model the temporal structure of different facial expressions from real-life data in the presence of translational and rotational errors that arise from head-tracking. The results demonstrate a 44% average performance increase over traditional optic flow methods for expressions extracted from unconstrained interactions.

[1]  T. Sejnowski,et al.  Measuring facial expressions by computer image analysis. , 1999, Psychophysiology.

[2]  Marian Stewart Bartlett,et al.  Classifying Facial Actions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[4]  Alex Pentland,et al.  Motion regularization for model-based head tracking , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Alex Pentland,et al.  A vision system for observing and extracting facial action parameters , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Bernt Schiele,et al.  Probabilistic object recognition using multidimensional receptive field histograms , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[7]  J. Cohn,et al.  Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding. , 1999, Psychophysiology.

[8]  A. Pentland,et al.  Real time tracking and modeling of faces: an EKF-based analysis by synthesis approach , 1999, Proceedings IEEE International Workshop on Modelling People. MPeople'99.

[9]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[10]  Larry S. Davis,et al.  Computing spatio-temporal representations of human faces , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  P. Ekman.  Unmasking The Face , 1975 .

[12]  Alex Pentland,et al.  Parametrized structure from motion for 3D adaptive feedback tracking of faces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.