Fusion of geometrical and texture information for facial expression recognition

A novel method based on geometrical and texture information is proposed for facial expression recognition from video sequences. The discriminant non-negative matrix factorization (DNMF) algorithm is applied at the image of the last frame of the video sequence, corresponding to the greatest intensity of the facial expression, thus extracting the texture information. A support vector machines (SVMs) system is used for the classification of the geometrical information derived from tracking the Candide grid over the video sequence. The geometrical information consists of the differences of the node coordinates between the neutral (first) and the fully expressed facial expression (last) video frame. The fusion of texture and geometrical information obtained is performed using SVMs. The accuracy achieved is 98.7% when recognizing the six basic facial expressions.