Robust pose estimation for 3D face modeling from stereo sequences

In this paper we propose a robust pose estimation algorithm from 2D correspondences, which is a key issue of a 3D face modeling system from calibrated stereo sequences. The estimated rigid motion parameters are utilized to obtain the perspective projection of a generic face model, which is then matched with 2D clues extracted from the image under corresponding pose to decide the shape of a specified face. The main merits of our method are: (1) In order to obtain robust and accurate results under the situation of dramatic pose variation, we first evaluate the reliability of 2D tracker. Then after eliminating erroneous 2D correspondences, we refine the rigid motion parameters estimated between successive poses by performing a non-linear, batch estimator to compute the parameters of all poses in a clip of stereo sequences simultaneously. (2) Full automaticity is achieved by detecting and matching new features when there are no enough reliable 2D tracking results. Experiments show that this algorithm is accurate and robust, and help our system reach satisfactory face modeling result.