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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 …