Classifying facial expressions using point-based analytic face model and Support Vector Machines

This paper describes a fully automated method of classifying facial expressions using support vector machines (SVM). Facial expressions are communicated by subtle changes in one or more discrete features such as tightening the lips, raising the eyebrows, opening and closing of the eyes or certain combination of them, which can be identified through monitoring the changes in muscles movement located near about the regions of mouth, eyes and eyebrows. In this work, we have applied an analytic face model using eleven feature points that represent and identify the principle muscle actions as well as provide measurements of the discrete features responsible for each of the six basic human emotions. A multi-detector approach of facial feature point localization has been utilized for identifying these points of interest from the contours of facial components such as eyes, eyebrows and mouth. Feature vectors composed of eleven features are then obtained by calculating the degree of displacement of these eleven feature points from a non-changeable rigid point. Finally, the obtained feature sets are used to train a SVM classifier so that it can classify facial expressions when given to it in the form of a feature set. The method has been tested on two different publicly available facial expression databases and on average, 89.44% and 84.86% of successful recognition rates have been achieved.