A new view-invariant feature for cross-view gait recognition

Kusakunniran, W; Wu, Q; Zhang, J; Ma, Y; Li, H

A new view-invariant feature for cross-view gait recognition

Kusakunniran, W

Wu, Q

Zhang, J

Ma, Y Li, H

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2013, 8 (10), pp. 1642 - 1653
Issue Date:: 2013-09-27

Closed Access

	Filename	Description	Size
	2013006327OK.pdf		1.57 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Kusakunniran, W https://orcid.org/0000-0002-2896-611X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Ma, Y	en_US
dc.contributor.author	Li, H	en_US
dc.date.issued	2013-09-27	en_US
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2013, 8 (10), pp. 1642 - 1653	en_US
dc.identifier.issn	1556-6013	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29299
dc.identifier.uri	http://hdl.handle.net/10453/27503
dc.description.abstract	Human gait is an important biometric feature which is able to identify a person remotely. However, change of view causes significant difficulties for recognizing gaits. This paper proposes a new framework to construct a new view-invariant feature for cross-view gait recognition. Our view-normalization process is performed in the input layer (i.e., on gait silhouettes) to normalize gaits from arbitrary views. That is, each sequence of gait silhouettes recorded from a certain view is transformed onto the common canonical view by using corresponding domain transformation obtained through invariant low-rank textures (TILTs). Then, an improved scheme of procrustes shape analysis (PSA) is proposed and applied on a sequence of the normalized gait silhouettes to extract a novel view-invariant gait feature based on procrustes mean shape (PMS) and consecutively measure a gait similarity based on procrustes distance (PD). Comprehensive experiments were carried out on widely adopted gait databases. It has been shown that the performance of the proposed method is promising when compared with other existing methods in the literature. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security	en_US
dc.relation.isbasedon	10.1109/TIFS.2013.2252342	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	A new view-invariant feature for cross-view gait recognition	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	8	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

Human gait is an important biometric feature which is able to identify a person remotely. However, change of view causes significant difficulties for recognizing gaits. This paper proposes a new framework to construct a new view-invariant feature for cross-view gait recognition. Our view-normalization process is performed in the input layer (i.e., on gait silhouettes) to normalize gaits from arbitrary views. That is, each sequence of gait silhouettes recorded from a certain view is transformed onto the common canonical view by using corresponding domain transformation obtained through invariant low-rank textures (TILTs). Then, an improved scheme of procrustes shape analysis (PSA) is proposed and applied on a sequence of the normalized gait silhouettes to extract a novel view-invariant gait feature based on procrustes mean shape (PMS) and consecutively measure a gait similarity based on procrustes distance (PD). Comprehensive experiments were carried out on widely adopted gait databases. It has been shown that the performance of the proposed method is promising when compared with other existing methods in the literature. © 2013 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/27503