Spontaneous facial expression classification with facial motion vectors

This paper proposes a novel spontaneous facial expression classification method using the facial motion magnification which transforms the subtle facial expressions into the corresponding exaggerated facial expressions. Facial motion magnification consists of four steps: First, we perform the active appearance model (AAM) fitting to extract 70 facial feature points in the face image sequence. Second, we align the face image sequence using the static three feature points. Third, we estimate the motion vectors of 27 feature points using the feature point tracking method. Finally, we obtain the exaggerated facial expressions by magnifying the motion vectors of the 27 feature points. After facial motion magnification, we recognize the exaggerated facial expressions using the support vector machines (SVM) to classify the facial expression features. Experimental results of the subtle facial expression recognition show promising results of the proposed method.