Hierarchical Image Matching for Pose-invariant Face Recognition

The paper addresses the problem of face recognition under arbitrary pose. A hierarchical MRF-based image matching method for finding pixel-wise correspondences between facial images viewed from different angles is proposed and used to densely register a pair of facial images. The goodness-of-match between two faces is then measured in terms of the normalized energy of the match which is a combination of both structural differences between faces as well as their texture distinctiveness. The method needs no training on non-frontal images and circumvents the need for geometrical normalization of facial images. It is also robust to moderate scale changes between images. The proposed approach is evaluated on the CMU PIE database and promising results are obtained.