Pose-robust face recognition based on texture mapping

A human face provides a variety of different communicative functions such as identification, the perception of emotional expression, and lip-reading. Many applications in robotics require recognizing a human face. A face recognition system should be able to deal with various changes in face images, such as pose, illumination, and expression, among which pose variation is the most difficult one to deal with. For this reason, face registration is the key of face recognition. If we can register face images into frontal views, the recognition task would be much easier. To align a face image into a canonical frontal view, we need to know the pose information of a human head. A human head can be approximately modeled as a 3D texture mapped ellipsoid. Then, any face image can be considered as a 2D image projection of a 3D ellipsoid at a certain pose. In this paper, both training and test face images are back projected to the surface of a 3D ellipsoid according to their estimated poses and registered into canonical frontal view images. Then, simple and efficient frontal face recognition can be carried out in the texture map domain instead of the original input image domain. To evaluate the feasibility of the proposed approach, several recognition experiments are conducted using subspace-based face recognition methods such as PCA, PCA+LAD, and DCV. By conducting experiments on our laboratory database and the Yale Face Database B, we show that the proposed algorithm provides good performance even when large out-of-plane rotations occur.