Real time facial expression recognition from image sequences using support vector machines

In this paper, a real-time method is proposed as a solution to the problem of facial expression classiffication in video sequences. The user manually places some of the Candide grid nodes to the face depicted at the first frame. The grid adaptation system, based on deformable models, tracks the entire Candide grid as the facial expression evolves through time, thus producing a grid that corresponds to the greatest intensity of the facial expression, as shown at the last frame. Certain points that are involved into creating the Facial Action Units movements are selected. Their geometrical displacement information, de ned as the coordinates' difference between the last and the first frame, is extracted to be the input to a six class Support Vector Machine system. The output of the system is the facial expression recognized. The proposed real-time system, recognizes the 6 basic facial expressions with an approximately 98% accuracy.