Facial feature tracking for Emotional Dynamic Analysis

Abstract. This article presents a feature-based framework to automatically track 18 facial landmarks for emotion recognition and emotional dynamic analysis. With a new way of using multi-kernel learning, we combine two methods: the first matches facial feature points between consecutive images and the second uses an offline learning of the facial landmark appearance. Matching points results in a jitter-free tracking and the offline learning prevents the tracking framework from drifting. We train the tracking system on the Cohn-Kanade database and analyze the dynamic of emotions and Action Units on the MMI database sequences. We perform accurate detection of facial expressions temporal segment and report experimental results