Fractional discriminative multiview correlation projection for face feature fusion

Yuan, YH; Lv, Q; Li, Y; Li, B; Ji, HK; Shen, XB

Fractional discriminative multiview correlation projection for face feature fusion

Yuan, YH Lv, Q Li, Y Li, B Ji, HK Shen, XB

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Publication Type:: Conference Proceeding
Citation:: 20th International Conference on Information Fusion, Fusion 2017 - Proceedings, 2017
Issue Date:: 2017-08-11

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Field	Value	Language
dc.contributor.author	Yuan, YH	en_US
dc.contributor.author	Lv, Q	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Li, B	en_US
dc.contributor.author	Ji, HK	en_US
dc.contributor.author	Shen, XB https://orcid.org/0000-0001-8494-4532	en_US
dc.date.issued	2017-08-11	en_US
dc.identifier.citation	20th International Conference on Information Fusion, Fusion 2017 - Proceedings, 2017	en_US
dc.identifier.isbn	9780996452700	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127340
dc.description.abstract	© 2017 International Society of Information Fusion (ISIF). Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter matrices with class label information of the samples. FDMCP first defines supervised covariance matrices in each view. It then constructs fractional supervised scatter matrices. Experimental results on three benchmark face image datasets show that our proposed FDMCP approach outperforms generalized multiview linear discriminant analysis.	en_US
dc.relation.ispartof	20th International Conference on Information Fusion, Fusion 2017 - Proceedings	en_US
dc.relation.isbasedon	10.23919/ICIF.2017.8009815	en_US
dc.title	Fractional discriminative multiview correlation projection for face feature fusion	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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

Abstract:

© 2017 International Society of Information Fusion (ISIF). Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter matrices with class label information of the samples. FDMCP first defines supervised covariance matrices in each view. It then constructs fractional supervised scatter matrices. Experimental results on three benchmark face image datasets show that our proposed FDMCP approach outperforms generalized multiview linear discriminant analysis.

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http://hdl.handle.net/10453/127340