Das Choudhury, Sruti and Tjahjadi, Tardi (2013) Gait recognition based on shape and motion analysis of silhouette contours. Computer Vision and Image Understanding, Volume 117 (Number 12). pp. 1770-1785. doi:10.1016/j.cviu.2013.08.003

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Abstract

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Gait in humans , Silhouettes , Video surveillance
Journal or Publication Title:	Computer Vision and Image Understanding
Publisher:	Elsevier Science Inc.
ISSN:	1077-3142
Official Date:	2013
Dates:	Date Event 2013 Published
Date of first compliant deposit:	25 December 2015
Volume:	Volume 117
Number:	Number 12
Page Range:	pp. 1770-1785
DOI:	10.1016/j.cviu.2013.08.003
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	University of Warwick

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