Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration

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

Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration

Kusakunniran, W

Wu, Q

Zhang, J

Li, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 545 - 548
Issue Date:: 2011-12-01

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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	Li, H	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 545 - 548	en_US
dc.identifier.isbn	9781457713033	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19155
dc.description.abstract	Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2011.6116403	en_US
dc.title	Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brussels, Belgium	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:

Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures. © 2011 IEEE.

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http://hdl.handle.net/10453/19155