Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

	Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition
	Ran He(赫然)1 ; Weishi Zheng 2,3; Baogang Hu,1 ; Xiangwei Kong 4; He, Ran
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2013
卷号	24 期号:1 页码:35-46
文章类型	Article
摘要	This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a robust metric in which noise and outliers in image pixels are detected. Potential loss functions, including L-1, L-2,L-1, and correntropy are studied. In the second stage, based on the learned metric and collaborative representation, we propose an efficient nonnegative sparse representation algorithm to find an approximation solution of sparse representation. According to the L1 ball theory in sparse representation, the approximated solution is unique and can be optimized efficiently. Then a filtering strategy is developed to avoid the computation of the sparse representation on the whole large-scale dataset. Moreover, theoretical analysis also gives the necessary condition for nonnegative least squares technique to find a sparse solution. Extensive experiments on several public databases have demonstrated that the proposed TSR approach, in general, achieves better classification accuracy than the state-of-the-art sparse representation methods. More importantly, a significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.
关键词	Correntropy L-1 Regularization Large-scale Nonnegative Sparse Representation Robust Face Recognition
WOS标题词	Science & Technology ; Technology
关键词[WOS]	LEAST-SQUARES PROBLEMS ; FRAMEWORK ; CLASSIFICATION ; CORRENTROPY ; REGRESSION ; UNIQUENESS ; EQUATIONS ; SYSTEMS ; OBJECTS ; SIGNAL
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000312844800004
引用统计	被引频次：104[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2856
专题	多模态人工智能系统全国重点实验室_多媒体计算模式识别实验室离退休人员
通讯作者	He, Ran
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510275, Guangdong, Peoples R China 3.Sun Yat Sen Univ, Guangdong Prov Key Lab Computat Sci, Guangzhou 510275, Guangdong, Peoples R China 4.Dalian Univ Technol, Sch Elect & Informat Engn, Dalian 116024, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Ran He,Weishi Zheng,Baogang Hu,,et al. Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2013,24(1):35-46.
APA	Ran He,Weishi Zheng,Baogang Hu,,Xiangwei Kong,&He, Ran.(2013).Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,24(1),35-46.
MLA	Ran He,et al."Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 24.1(2013):35-46.

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