Abstract
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint. Next, a locality LMNN Weighted Sparse Representation based Classification (LMNN-WSRC) and a locality LMNN Weighted Collaborative Representation based Classification (LMNN-WCRC) are proposed. Our methods utilize both linearity and data locality. For a query face image, our target is to exploit the appropriate distance metric as the locality constraint that could focus more on those truly related images in the code book. Experimental results on the Extended Yale B database and the AR database show that our methods are more effective than SRC, Weighted SRC (WSRC) and CRC.