Adaptive fusion of gait and face for human identification in video

Geng, X; Wang, L; Li, M; Wu, Q; Smith-Miles, K

Adaptive fusion of gait and face for human identification in video

Geng, X Wang, L Li, M Wu, Q

Smith-Miles, K

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Publication Type:: Conference Proceeding
Citation:: 2008 IEEE Workshop on Applications of Computer Vision, WACV, 2008
Issue Date:: 2008-09-08

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Field	Value	Language
dc.contributor.author	Geng, X	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Li, M	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Smith-Miles, K	en_US
dc.date.issued	2008-09-08	en_US
dc.identifier.citation	2008 IEEE Workshop on Applications of Computer Vision, WACV, 2008	en_US
dc.identifier.isbn	1424419131	en_US
dc.identifier.isbn	9781424419135	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10829
dc.description.abstract	Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.	en_US
dc.relation.ispartof	2008 IEEE Workshop on Applications of Computer Vision, WACV	en_US
dc.relation.isbasedon	10.1109/WACV.2008.4544006	en_US
dc.title	Adaptive fusion of gait and face for human identification in video	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Copper Mountain, CO, USA	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.

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http://hdl.handle.net/10453/10829