《计算机应用研究》|Application Research of Computers

针对混合污染的结构化鲁棒低秩恢复算法在人脸识别中的应用

Structured robust low-rank recovery algorithm for face recognition with mixed contaminations

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作者 吴小艺,吴小俊,陈哲
机构 江南大学 物联网工程学院,江苏 无锡 214122
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文章编号 1001-3695(2020)09-060-2851-05
DOI 10.19734/j.issn.1001-3695.2019.04.0165
摘要 传统的低秩恢复算法在识别有混合污染的人脸图像时,通常只对污染部分进行一种类型的约束,并不能很好地恢复出干净的样本。针对这种情况,提出了结构化鲁棒低秩恢复算法(structured and robust low-rank recovery for mixed contamination,SRLRR)。SRLRR算法利用对二维误差图像的低秩约束移除样本中的连续污染部分,同时利用稀疏约束分离样本中服从拉普拉斯分布的噪声。另外,为了学习到更具有鉴别性的低秩表示,该算法对表示系数进行了块对角结构化约束。在三个常用数据库上的实验证明了SRLRR算法的有效性和鲁棒性。
关键词 混合污染; 人脸识别; 结构化约束; 低秩恢复
基金项目 国家自然科学基金资助项目(61672265,U1836218)
国家教育部111资助项目(B12018)
本文URL http://www.arocmag.com/article/01-2020-09-060.html
英文标题 Structured robust low-rank recovery algorithm for face recognition with mixed contaminations
作者英文名 Wu Xiaoyi, Wu Xiaojun, Chen Zhe
机构英文名 School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China
英文摘要 When there exist mixed contaminations in face images, traditional low-rank recovery algorithms usually imposes only one constraint on the corresponding contaminations, it cannot recover clean samples very well. In order to solve this problem, this paper proposed a structured robust low-rank recovery algorithm(SRLRR). The SRLRR algorithm imposed low-rank constraint on the 2D error image to remove the continuous contamination, and introduced sparse constraint to separate the noise that obeyed the Laplacian distribution in samples. Moreover, the proposed algorithm imposed a block-diagonal structured constraint on the representation coefficient to learn the more discriminative low-rank representation. The experimental results on three commonly and using standard databases verify the effectiveness and robustness of the proposed SRLRR algorithm.
英文关键词 mixed contaminations; face recognition; structured constraint; low-rank recovery
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收稿日期 2019/4/9
修回日期 2019/6/3
页码 2851-2855,2865
中图分类号 TP391
文献标志码 A