Symmetry-based face pose estimation from a single uncalibrated view

In this paper, a geometric method for estimating the face pose (roll and yaw angles) from a single uncalibrated view is presented. The symmetric structure of the human face is exploited by taking the mirror image (horizontal flip) of a test face image as a virtual second view. Facial feature point correspondences are established between the given test and its mirror image using an active appearance model. Thus, the face pose estimation problem is cast as a two-view rotation estimation problem. By using the bilateral symmetry, roll and yaw angles are estimated without the need for camera calibration. The proposed pose estimation method is evaluated on synthetic and natural face datasets, and the results are compared with an eigenspace-based method. It is shown that the proposed symmetry-based method shows performance that is comparable to the eigenspace-based method for both synthetic and real face image datasets.