A novel multi-view dimensionality reduction and recognition framework with applications to face recognition

Shen, X; Yuan, YH; Shen, F; Xu, Y; Sun, QS

A novel multi-view dimensionality reduction and recognition framework with applications to face recognition

Shen, X

Yuan, YH Shen, F Xu, Y Sun, QS

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Publication Type:: Journal Article
Citation:: Journal of Visual Communication and Image Representation, 2018, 53 pp. 161 - 170
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Shen, X https://orcid.org/0000-0001-8494-4532	en_US
dc.contributor.author	Yuan, YH	en_US
dc.contributor.author	Shen, F	en_US
dc.contributor.author	Xu, Y	en_US
dc.contributor.author	Sun, QS	en_US
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	Journal of Visual Communication and Image Representation, 2018, 53 pp. 161 - 170	en_US
dc.identifier.issn	1047-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129034
dc.description.abstract	© 2018 Elsevier Inc. Multi-view data with each view corresponding to a type of feature generally provides more comprehensive information. Learning from multi-view data is a challenging research topic in pattern recognition. For recognition task, most multi-view learning methods separately learn multi-view dimensionality reduction (MvDR) and classification models. Thus, the connection between the two models has not been well studied. In this paper, we propose a novel multi-view dimensionality reduction and recognition framework, which can establish the connection between MvDR and classification. Specifically, a multi-view dimensionality reduction method, termed as sparse representation regularized multiset canonical correlation analysis (SR2MCC) is first proposed. SR2MCC considers both correlation and sparse discrimination among multiple views. In accord with SR2MCC, a classifier, called multi-view sparse representation based classifier (MvSRC) is further developed. MvSRC performs classification by comparing the reconstruction residuals of different classes among all views. An efficient iterative algorithm is proposed to solve the proposed model. Extensive experiments on the AR, CMU PIE, FERET, and FRGC datasets demonstrate that the proposed framework can achieve superior recognition performance than several state-of-the-art methods.	en_US
dc.relation.ispartof	Journal of Visual Communication and Image Representation	en_US
dc.relation.isbasedon	10.1016/j.jvcir.2018.03.004	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A novel multi-view dimensionality reduction and recognition framework with applications to face recognition	en_US
dc.type	Journal Article
utslib.citation.volume	53	en_US
utslib.for	1905 Visual Arts and Crafts	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1203 Design Practice and Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	53	en_US

Abstract:

© 2018 Elsevier Inc. Multi-view data with each view corresponding to a type of feature generally provides more comprehensive information. Learning from multi-view data is a challenging research topic in pattern recognition. For recognition task, most multi-view learning methods separately learn multi-view dimensionality reduction (MvDR) and classification models. Thus, the connection between the two models has not been well studied. In this paper, we propose a novel multi-view dimensionality reduction and recognition framework, which can establish the connection between MvDR and classification. Specifically, a multi-view dimensionality reduction method, termed as sparse representation regularized multiset canonical correlation analysis (SR2MCC) is first proposed. SR2MCC considers both correlation and sparse discrimination among multiple views. In accord with SR2MCC, a classifier, called multi-view sparse representation based classifier (MvSRC) is further developed. MvSRC performs classification by comparing the reconstruction residuals of different classes among all views. An efficient iterative algorithm is proposed to solve the proposed model. Extensive experiments on the AR, CMU PIE, FERET, and FRGC datasets demonstrate that the proposed framework can achieve superior recognition performance than several state-of-the-art methods.

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http://hdl.handle.net/10453/129034