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

In this paper, two novel real-time methods are proposed for facial expression recognition in image sequences. The user manually places some of the Candide grid's points to the face depicted at the first frame. The grid adaptation system tracks the entire 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 (FAUs) movements are selected. Their geometrical displacement information, defined as the coordinates' difference between the last and the first frame, is extracted to be the input to a bank of support vector machine (SVM) classifiers that are used to recognize either the six basic facial expressions or eight chosen FAUs. The results show a recognition accuracy of approximately 98% and 94% for direct and FAU based facial expression recognition, respectively.