3D model based head pose tracking by using weighted depth and brightness constraints

This paper proposes a robust method of tracking human head poses from a sequence of monocular images. First we estimate the head pose parameters in the first frame by an affine correspondence based method developed in our lab. Then both the linear brightness and depth constraint equations derived from the small interframe rigid motion assumption are used to implement the fast tracking of the head poses. We also take advantage of geometry information of the features on the face surface to weight the brightness and depth constraint equations to get more accurate results. Finally, in order to diminish the effects of gradual illumination changes and occlusions, we estimate the reliability of the features frame by frame and dynamically update the reliable feature set. Experiments show the proposed method can robustly track the head poses especially for the types of motions which make obvious depth variation.