Matrix regression-based classification with block-norm

• Published in: Pattern recognition letters Vol. 125; pp. 654 - 660
• Main Authors: Mi, Jian-Xun, Zhu, Quanwei, Luo, Zhiheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2019
• Subjects: Block-norm; Face recognition; Regression; Block-norm; Face recognition; 41A10; Regression; 65D05; 65D17; 41A05
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2019.07.007

•A new matrix-based sparse representation classification approach is pro-posed.•Block-norm depresses the occlusion parts of an image and utilizes the rest parts.•A self-adaptive threshold is employed to restrict large residual error. In this paper, we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block). Specifically, we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods. In the regression step, a block-norm based matrix regression model is proposed, which can efficiently depress the effect of occlusion in probe images. Accordingly, an efficient algorithm is derived to optimize the proposed objective function. Comprehensive experiments on representative datasets demonstrate that MRC-block is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face image with occlusion.