A sparse regularized nuclear norm based matrix regression for face recognition with contiguous occlusion

摘要

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 l(1)-norm can uncover more natural sparsity of representations than l(2)-norm, we propose a sparse regularized NMR (SR_NMR) algorithm by imposing the l(1)-norm constraint rather than l(2)-norm on the representations of NMR framework. SR_NMR seamlessly integrates the nuclear norm based error matrix regression and l(1)-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. (C) 2019 Published by Elsevier B.V.