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View and clothing invariant gait recognition via 3D human semantic folding
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Luo, Jian and Tjahjadi, Tardi (2020) View and clothing invariant gait recognition via 3D human semantic folding. IEEE Access, 8 . pp. 100365-100383. doi:10.1109/ACCESS.2020.2997814 ISSN 2169-3536.
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WRAP-View-clothing-invariant-gait-recognition-via-human-semantic-folding-Tjahjadi-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1350Kb) | Preview |
Official URL: https://doi.org/10.1109/ACCESS.2020.2997814
Abstract
A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2-dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > QP Physiology T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Gait in humans, Gait in humans -- Computer simulation, Pattern recognition systems, Biometric identification, Computer vision, Three-dimensional modeling | ||||||||||||||||||
| Journal or Publication Title: | IEEE Access | ||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||
| ISSN: | 2169-3536 | ||||||||||||||||||
| Official Date: | 26 May 2020 | ||||||||||||||||||
| Dates: |
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| Volume: | 8 | ||||||||||||||||||
| Page Range: | pp. 100365-100383 | ||||||||||||||||||
| DOI: | 10.1109/ACCESS.2020.2997814 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 1 June 2020 | ||||||||||||||||||
| Date of first compliant Open Access: | 1 June 2020 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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