Human gait recognition with matrix representation

Xu, D; Yan, S; Tao, D; Zhang, L; Li, X; Zhang, HJ

Human gait recognition with matrix representation

Xu, D

Yan, S Tao, D

Zhang, L Li, X Zhang, HJ

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2006, 16 (7), pp. 896 - 903
Issue Date:: 2006-07-01

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Field	Value	Language
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Yan, S	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Zhang, HJ	en_US
dc.date.issued	2006-07-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2006, 16 (7), pp. 896 - 903	en_US
dc.identifier.issn	1051-8215	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15224
dc.description.abstract	Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology	en_US
dc.relation.isbasedon	10.1109/TCSVT.2006.877418	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Human gait recognition with matrix representation	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	16	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database. © 2006 IEEE.

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http://hdl.handle.net/10453/15224