[1]WU Xiaoyi,WU Xiaojun.A low rank recovery algorithm for face recognition with structured and weighted sparse constraint[J].CAAI Transactions on Intelligent Systems,2019,14(3):455-463.[doi:10.11992/tis.201711026]
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A low rank recovery algorithm for face recognition with structured and weighted sparse constraint

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 455-463 Column: 学术论文—机器感知与模式识别 Public date: 2019-05-05
Title:
A low rank recovery algorithm for face recognition with structured and weighted sparse constraint
Author(s):
WU Xiaoyi;  WU Xiaojun
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
face recognition; structured; weighted sparse; low-rank representation; subspace projection
CLC:
TP391
DOI:
10.11992/tis.201711026
Abstract:
Herein, a structured and weighted sparse low-rank recovery algorithm (SWLRR) is proposed to deal with trained or tested samples that are corrupt. The SWLRR constrains the low-rank representation by incorporating the structured and weighted sparse constraints, enabling the low-rank representation coefficient matrix to be closer to the block diagonal. Then, a discriminative structured representation can be obtained. After recovering the clean training samples from the corrupted training samples using SWLRR, the low-rank projection matrix is learnt by the low-rank projection matrix according to the original and recovered training samples, whereas the test samples are projected into the corresponding low-rank subspaces. In this way, the corrupted regions can be removed efficiently from the test samples. The experimental results on several face databases validate the effectiveness and robustness of the SWLRR under different situations.
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