Abstract
•MBy utilizing the NMR model, we can make full use of the low-rank structural information of contiguous error images.•Different to L2-norm based NMR, we simultaneously exploit low-rank error images and sparse regression representations.•Experiments show pursuing sparse representations is more helpful to remove low-rank contiguous noise.
Nuclear norm based matrix regression (NMR) method has been proposed to alleviate the influence of contiguous occlusion on face recognition problems. NMR considers that the error image of a test sample has low-rank structure due to the contiguous nature of occlusion. Based on the observation that l1-norm can uncover more natural sparsity of representations than l2-norm, we propose a sparse regularized NMR (SR_NMR) algorithm by imposing the l1-norm constraint rather than l2-norm on the representations of NMR framework. SR_NMR seamlessly integrates the nuclear norm based error matrix regression and l1-norm based sparse representation into one joint framework. Finally, we use the training samples to learn a linear classifier to implement efficient classification. Extensive experiments on three face databases show the proposed SR_NMR can achieve better recognition performance compared with the traditional NMR and other regression methods which indicates that sparse representations are very helpful to recover low-rank error images in the presence of severe occlusion and illumination changes.