Robust facial expression recognition based on 3-d supported feature extraction and SVM classification

Facial expression recognition is an imperative task in human computer interaction systems. In this work we propose a new system for automatic expression recognition in video sequences. Our system uses color information to extract the facial features. Additionally, it includes a camera model and a registration step, in which we automatically build a person specific face model from stereo. Photogrammetric techniques are applied to determine real world geometric measures and to build the feature vector. Feature normalization is carried out and support vector machine is trained over the normalized feature vector. Using SVM classification we reach a minimal mixing between different expression classes. Our framework achieves robust and superior expression classification results across a variety of head poses with resulting perspective foreshortening and changing face size. Moreover, the method has shown robustness across a variety of skin colors while reaching high performance.