Real-time facial features tracker with motion estimation and feedback

Real-time automatic face tracking is a great challenge in computer vision and computer graphics. We develop a system to automatically track the face by integrating auto-generation of features of first frame, feature correspondence and Kalman filter based on face attribution and motion estimation. First, facial features of the first frame are acquired automatically by face detachment and Plessey corner detector according to FDP in MPEG-4 based on anatomical knowledge and general 3D model. Feature correspondence is obtained by maximizing the cross-correlation and Kalman filter. Automatic face detection makes full use of the knowledge of 3D general model and face attribute, and face tracking is more efficient by using motion estimation. Experimental results show the high prospect of this algorithm.