Estimating face pose by facial asymmetry and geometry

A robust pose estimation approach is proposed by combining facial appearance asymmetry and 3D geometry in a coarse-to-fine framework. The rough face pose is first estimated by analyzing the asymmetry of the distribution of the facial component detection confidences on an image, which actually implies an intrinsic relation between the face pose and the facial appearance. Then, this rough face pose, as well as error bandwidth, is utilized into a 3D-to-2D geometrical model matching to refine the pose estimation. The proposed approach is able to track a face with fast motion in front of cluttered background and recover its pose robustly and accurately in real- time. Experiment results are provided to demonstrate its efficiency and accuracy.