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Robust arbitrary-view gait recognition based on 3D partial similarity matching

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Tang, Jin, Luo, Jian, Tjahjadi, Tardi and Guo, Fan (2017) Robust arbitrary-view gait recognition based on 3D partial similarity matching. IEEE Transactions on Image Processing, 26 (1). pp. 7-22. doi:10.1109/TIP.2016.2612823

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Official URL: http://dx.doi.org/10.1109/TIP.2016.2612823

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Abstract

Existing view-invariant gait recognition methods encounter difficulties due to limited number of available gait views and varying conditions during training. This paper proposes gait partial similarity matching that assumes a 3-dimensional (3D) object shares common view surfaces in significantly different views. Detecting such surfaces aids the extraction of gait features from multiple views. 3D parametric body models are morphed by pose and shape deformation from a template model using 2-dimensional (2D) gait silhouette as observation. The gait pose is estimated by a level set energy cost function from silhouettes including incomplete ones. Body shape deformation is achieved via Laplacian deformation energy function associated with inpainting gait silhouettes. Partial gait silhouettes in different views are extracted by gait partial region of interest elements selection and re-projected onto 2D space to construct partial gait energy images. A synthetic database with destination views and multi-linear subspace classifier fused with majority voting are used to achieve arbitrary view gait recognition that is robust to varying conditions. Experimental results on CMU, CASIA B, TUM-IITKGP, AVAMVG and KY4D datasets show the efficacy of the propose method.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QP Physiology
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Gait in humans -- Computer simulation, Humans -- Identification , Three-dimensional modeling
Journal or Publication Title: IEEE Transactions on Image Processing
Publisher: IEEE
ISSN: 1057-7149
Official Date: January 2017
Dates:
DateEvent
January 2017Published
22 September 2016Available
6 September 2016Accepted
Volume: 26
Number: 1
Page Range: pp. 7-22
DOI: 10.1109/TIP.2016.2612823
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Hunan, China (Province). Science and Technology Program
Grant number: Grant No.15C0981 (NSFC), No.2015WK30 06

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