Bayesian versus support vector machine based approaches for facial feature classification in image sequences

A method for automatic facial expression recognition in image sequences, is introduced which make use of Candide wire frame model and active appearance algorithm for tracking, and Bayesian classifier for classification. On the first frame of face image sequence, Candide wire frame model is adapted properly. In subsequent frames of image sequence, facial features are tracked using active appearance algorithm. The algorithm adapts Candide wire frame model to the face in each of the frames and tracks the grid in consecutive video frames over time. Last frame of image sequence corresponds to greatest facial expression intensity. The difference of the node coordinates between the first and the greatest facial expression intensity frame, called the geometrical displacement of Candide wire frame nodes is used as an input to a classifier, which classifies facial expression into one of the class such as happy, surprise, sad, anger, disgust and fear. The experimental results show that the proposed method is better in classification correctness in comparison with binary SVM tree classifier.