Robust Gait Recognition under Unconstrained Environments Using Hybrid Descriptions

Yao, L; Kusakunniran, W; Wu, Q; Zhang, J; Tang, Z

Robust Gait Recognition under Unconstrained Environments Using Hybrid Descriptions

Yao, L Kusakunniran, W Wu, Q

Zhang, J

Tang, Z

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Publication Type:: Conference Proceeding
Citation:: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7
Issue Date:: 2017-12-19

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Field	Value	Language
dc.contributor.author	Yao, L	en_US
dc.contributor.author	Kusakunniran, W	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	Tang, Z	en_US
dc.date.issued	2017-12-19	en_US
dc.identifier.citation	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7	en_US
dc.identifier.isbn	9781538628393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122179
dc.description.abstract	© 2017 IEEE. Gait is one of the key biometric features that has been widely applied for human identification. Appearance-based features and motion-based features are the two mainly used presentations in the gait recognition. However, appearance-based features are sensitive to the body shape changes and silhouette extraction from real-world images and videos also remains a challenge. As for motion features, due to the difficulty in extracting the underlying models from gait sequences, the localization of human joints lacks of high reliability and strong robustness. This paper proposes a new approach which utilizes Two-Point Gait (TPG) as the motion feature to remedy the deficiency of the appearance feature based on Gait Energy Image (GEI), in order to increase the robustness of gait recognition under the unconstrained environments with view changes and cloth changes. Another contribution of this paper is that this is the first time that TPG has been applied for view change and cloth change issues since it was proposed. The extensive experiments show that the proposed method is more invariant to the view change and cloth change, and can significantly improve the robustness of gait recognition.	en_US
dc.relation.ispartof	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2017.8227486	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Robust Gait Recognition under Unconstrained Environments Using Hybrid Descriptions	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

© 2017 IEEE. Gait is one of the key biometric features that has been widely applied for human identification. Appearance-based features and motion-based features are the two mainly used presentations in the gait recognition. However, appearance-based features are sensitive to the body shape changes and silhouette extraction from real-world images and videos also remains a challenge. As for motion features, due to the difficulty in extracting the underlying models from gait sequences, the localization of human joints lacks of high reliability and strong robustness. This paper proposes a new approach which utilizes Two-Point Gait (TPG) as the motion feature to remedy the deficiency of the appearance feature based on Gait Energy Image (GEI), in order to increase the robustness of gait recognition under the unconstrained environments with view changes and cloth changes. Another contribution of this paper is that this is the first time that TPG has been applied for view change and cloth change issues since it was proposed. The extensive experiments show that the proposed method is more invariant to the view change and cloth change, and can significantly improve the robustness of gait recognition.

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http://hdl.handle.net/10453/122179