Pose and illumination variable face recognition via sparse representation and illumination dictionary

Abstract

This paper addresses the problem of face recognition under pose and illumination variations, and proposes a novel algorithm inspired by the idea of sparse representation (SR). In order to make the SR early designed for the pose-invariant face recognition suitable for the case of pose variation, a multi-pose weighted sparse representation (MW-SR) algorithm is proposed to emphasize the contributions of the similar poses in the representation of the test image. Furthermore, when some illumination variations are added to the images, it is more reasonable to take advantage of the results of pose variable recognition and avoid the traditional SR method that adds all kinds of images with pose and illumination variations in the training dictionary. Here, a novel idea of the proposed algorithms is adding a general illumination dictionary to the training dictionary, and that once the illumination dictionary is designed, it is common for the other face databases. Extensive experiments illustrate that the proposed algorithms perform better than some existing methods for the face recognition under pose and illumination variations.