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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
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 | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology |
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| 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: |
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| Date of first compliant deposit: | 18 October 2016 | ||||||||
| 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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