Continuous Pose Normalization for Pose-Robust Face Recognition

Pose variation is a great challenge for robust face recognition. In this paper, we present a fully automatic pose normalization algorithm that can handle continuous pose variations and achieve high face recognition accuracy. First, an automatic method is proposed to find pose-dependent correspondences between 2-D facial feature points and 3-D face model. This method is based on a multi-view random forest embedded active shape model. Then we densely map each pixel in the face image onto the 3-D face model and rotate it to the frontal view. The filling of occluded face regions is guided by facial symmetry. Recognition experiments were conducted on the two western databases CMU-PIE, FERET and one eastern database CAS-PEAL. Currently the algorithm has been trained with pose variation up to ±50° in yaw. Our algorithm not only achieves high recognition accuracy for learnt poses but also shows good generalizability for extreme poses. Furthermore, it suggests the promising application to people of different races.