Spontaneous Facial Expression Recognition Based on Feature Point Tracking

In recent years, facial expression recognition has attracted a lot of attention because of its importance in human-computer interaction. However, most previous work has focus on posed expression. In this paper, we propose a spontaneous facial expression recognition method based on feature point tracking. First, all expression sequences are normalized according to their pupils´ coordinates. Second, 23 points are labeled manually in the onset and apex frames. Then Kalman filter is used for tracking. Two kinds of features, point displacement features and points distance variation features, are extracted. Finally, Hidden Markov Model is employed as classifier. Experiments have been conducted on the spontaneous facial expression database of USTC-NVIE. The results indicate that Kalman filter point tracking method could detect the right place of points, and the points distance variation features are more suitable than the point displacement features for the facial expression recognition.