Pairwise shape configuration-based PSA for gait recognition under small viewing angle change

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

Pairwise shape configuration-based PSA for gait recognition under small viewing angle change

Kusakunniran, W Wu, Q

Zhang, J

Li, H

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Publication Type:: Conference Proceeding
Citation:: 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011, 2011, pp. 17 - 22
Issue Date:: 2011-10-17

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Field	Value	Language
dc.contributor.author	Kusakunniran, W	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	Li, H	en_US
dc.date.issued	2011-10-17	en_US
dc.identifier.citation	2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011, 2011, pp. 17 - 22	en_US
dc.identifier.isbn	9781457708459	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19156
dc.description.abstract	Two main components of Procrustes Shape Analysis (PSA) are adopted and adapted specifically to address gait recognition under small viewing angle change: 1) Procrustes Mean Shape (PMS) for gait signature description; 2) Procrustes Distance (PD) for similarity measurement. Pairwise Shape Configuration (PSC) is proposed as a shape descriptor in place of existing Centroid Shape Configuration (CSC) in conventional PSA. PSC can better tolerate shape change caused by viewing angle change than CSC. Small variation of viewing angle makes large impact only on global gait appearance. Without major impact on local spatio-temporal motion, PSC which effectively embeds local shape information can generate robust view-invariant gait feature. To enhance gait recognition performance, a novel boundary re-sampling process is proposed. It provides only necessary re-sampled points to PSC description. In the meantime, it efficiently solves problems of boundary point correspondence, boundary normalization and boundary smoothness. This re-sampling process adopts prior knowledge of body pose structure. Comprehensive experiment is carried out on the CASIA gait database. The proposed method is shown to significantly improve performance of gait recognition under small viewing angle change without additional requirements of supervised learning, known viewing angle and multi-camera system, when compared with other methods in literatures. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011	en_US
dc.relation.isbasedon	10.1109/AVSS.2011.6027286	en_US
dc.title	Pairwise shape configuration-based PSA for gait recognition under small viewing angle change	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Klagenfurt, Austria	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.publication-status	Published	en_US

Abstract:

Two main components of Procrustes Shape Analysis (PSA) are adopted and adapted specifically to address gait recognition under small viewing angle change: 1) Procrustes Mean Shape (PMS) for gait signature description; 2) Procrustes Distance (PD) for similarity measurement. Pairwise Shape Configuration (PSC) is proposed as a shape descriptor in place of existing Centroid Shape Configuration (CSC) in conventional PSA. PSC can better tolerate shape change caused by viewing angle change than CSC. Small variation of viewing angle makes large impact only on global gait appearance. Without major impact on local spatio-temporal motion, PSC which effectively embeds local shape information can generate robust view-invariant gait feature. To enhance gait recognition performance, a novel boundary re-sampling process is proposed. It provides only necessary re-sampled points to PSC description. In the meantime, it efficiently solves problems of boundary point correspondence, boundary normalization and boundary smoothness. This re-sampling process adopts prior knowledge of body pose structure. Comprehensive experiment is carried out on the CASIA gait database. The proposed method is shown to significantly improve performance of gait recognition under small viewing angle change without additional requirements of supervised learning, known viewing angle and multi-camera system, when compared with other methods in literatures. © 2011 IEEE.

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http://hdl.handle.net/10453/19156