CLASSIFYING FACIAL EXPRESSIONS USING LEVEL SET METHOD BASED LIP CONTOUR DETECTION AND MULTI-CLASS SUPPORT VECTOR MACHINES

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This paper describes a fully automated computer vision system for detection and classification of the seven basic facial expressions using Multi-Class Support Vector Machine (SVM). Facial expressions are communicated by subtle changes in one or more discrete features such as tightening of the lips, raising the eyebrows, opening and closing of eyes or certain combination of them, which can be identified through monitoring the changes in muscle movements (Action Units), located around the regions of mouth, eyes and eyebrows. For classifying facial expressions, an analytic representation of face with 15 feature points has been used that provides visual observation of the discrete features responsible for the seven basic facial expressions. Feature points from the region of mouth are detected by segmenting the lip contour applying a variational formulation of the level set method. A multidetector approach of facial feature point detection is utilized for identifying the feature-points from the regions of eyes, eyebrows and nose. Feature vectors composed of 15 features are then obtained with respect to the average representation of neutral face and are used to train a Multiclass SVM classifier. The proposed method has been tested over two different facial expression image databases and the average successful recognition rates of 92.04% and 86.33% have been achieved.