LOCAL FEATURES AND SPARSE REPRESENTATION FOR FACE RECOGNITION WITH PARTIAL OCCLUSIONS

In this paper we present a new local-based face recognition system that combines weak classifiers to create a robust system able to recognize faces in presence of either occlusions or large expression variations. The method relies on sparse approximation using dictionaries built on local features. Experiments on the AR database show the effectiveness of our method, which achieves better performance than those obtained by the state-of-the-art l(1) norm-based sparse representation classifier (SRC).