Nuclear Norm Based Superposed Collaborative Representation Classifier for Robust Face Recognition

Abstract

In this paper, we propose a novel robust face recognition framework named nuclear norm based superposed collaborative representation classifier (NNSCRC) to handle illumination variations, occlusion and undersampled problems in face recognition. Specifically, we develop a superposed linear collaborative representation classifier for robust face recognition by representing the query image in terms of a superposition of the class centroid, the shared intra-class difference, and the low rank error. By representing a face image as the class centroid and the shared intra-class difference, our model can effectively enhance the face recognition performance on undersampled databases. In addition, since the occlusion and illumination variations generally lead to a low-rank error image, we use nuclear norm matrix regression to obtain these low-rank errors, which makes our model able to reconstruct the test image better. Extensive experiments are performed on Extended Yale-B and AR databases, which show the effectiveness of NNSCRC in robust face recognition.