Joint latent low-rank and non-negative induced sparse representation for face recognition

doi:10.1007/s10489-021-02338-x

	Joint latent low-rank and non-negative induced sparse representation for face recognition
	Wu, Mingna 2,3; Wang, Shu 2,3; Li, Zhigang 2; Zhang, Long 2; Wang, Ling 2; Ren, Zhenwen 1,4
刊名	APPLIED INTELLIGENCE
	2021-04-02
关键词	Face recognition Elastic net regularization Non-negative constraint Low-rank learning Sparse representation
ISSN号	0924-669X
DOI	10.1007/s10489-021-02338-x
通讯作者	Wang, Ling(wl@ipp.ac.cn) ; Ren, Zhenwen(rzw@njust.edu.cn)
英文摘要	Representation-based methods have achieved exciting results in recent applications of face recognition. However, it is still challenging for the face recognition task due to noise and outliers in the data. Many existing methods avoid these problems by constructing an auxiliary dictionary from the extended data but fail to achieve good performances because they use the main dictionary only for classification. In this paper, to avoid the need to manually construct an auxiliary dictionary and the effects of noise, we propose a Joint Latent Low-Rank and Non-Negative Induced Sparse Representation (JLSRC) for face recognition. Specifically, JLSRC adaptively learns two clean low-rank reconstructed dictionaries jointly via an extended latent low-rank representation to reveal the potential relationships in the data and then embeds a non-negative constraint and an Elastic Net regularization in the coefficient vectors of the dictionaries to enhance the performance on classification. In this way, the learned low-rank dictionaries can be mutually boosted to extract discriminative features and handle the noise, and the obtained coefficient vectors are simultaneously both sparse and discriminative. Moreover, the proposed method seamlessly and elegantly integrates low-rank learning and sparse representation-based classification. Extensive experiments on three challenging face databases demonstrate the effectiveness and robustness of JLSRC in comparison with the state-of-the-art methods.
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000635857900001
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/121509]
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Ling; Ren, Zhenwen
作者单位	1.Nanjing Univ Sci & Technol, Nanjing 210094, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Southwest Univ Sci & Technol, Mianyang 621010, Sichuan, Peoples R China
推荐引用方式 GB/T 7714	Wu, Mingna,Wang, Shu,Li, Zhigang,et al. Joint latent low-rank and non-negative induced sparse representation for face recognition[J]. APPLIED INTELLIGENCE,2021.
APA	Wu, Mingna,Wang, Shu,Li, Zhigang,Zhang, Long,Wang, Ling,&Ren, Zhenwen.(2021).Joint latent low-rank and non-negative induced sparse representation for face recognition.APPLIED INTELLIGENCE.
MLA	Wu, Mingna,et al."Joint latent low-rank and non-negative induced sparse representation for face recognition".APPLIED INTELLIGENCE (2021).