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

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition
	He, Ran 1; Zheng, Wei-Shi 2,3; Hu, Bao-Gang 1; Kong, Xiang-Wei 4; Ran He(赫然)
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2013
卷号	24 期号:1 页码:35-46
关键词	Correntropy L-1 regularization large-scale nonnegative sparse representation robust face recognition
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
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记录号	WOS:000312844800004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2856]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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
推荐引用方式 GB/T 7714	He, Ran,Zheng, Wei-Shi,Hu, Bao-Gang,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	He, Ran,Zheng, Wei-Shi,Hu, Bao-Gang,Kong, Xiang-Wei,&Ran He.(2013).Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,24(1),35-46.
MLA	He, Ran,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.