Adaptive Fusion of Gait and Face for Human Identification in Video

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dc.contributor.author Geng, X
dc.contributor.author Wang, L
dc.contributor.author Li, M
dc.contributor.author Wu, Q
dc.contributor.author Smith-Miles, K
dc.contributor.editor Stockman, G
dc.contributor.editor al, E
dc.date.accessioned 2010-05-28T09:59:50Z
dc.date.issued 2008-01
dc.identifier.citation IEEE Workshop on Applications of Computer Vision, 2008, 2008, pp. 1 - 6
dc.identifier.issn 1550-5790
dc.identifier.other E1 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.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/WACV.2008.4544006
dc.title Adaptive Fusion of Gait and Face for Human Identification in Video
dc.type Conference Proceeding
dc.parent IEEE Workshop on Applications of Computer Vision, 2008
dc.journal.number en_US
dc.publocation USA en_US
dc.publocation USA
dc.publocation USA
dc.identifier.startpage 1 en_US
dc.identifier.endpage 6 en_US
dc.cauo.name INEXT Research Strength Core en_US
dc.conference Verified OK en_US
dc.conference IEEE Workshop on Applications of Computer Vision
dc.conference IEEE Workshop on Applications of Computer Vision
dc.for 080106 Image Processing
dc.for 080104 Computer Vision
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 000748
dc.percentage 40 en_US
dc.classification.name Computer Vision en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE Workshop on Applications of Computer Vision en_US
dc.date.activity 20080107 en_US
dc.date.activity 2008-01-07
dc.date.activity 2008-01-07
dc.location.activity Copper Mountain, CO, USA en_US
dc.location.activity Copper Mountain, CO, USA
dc.location.activity Copper Mountain, CO, USA
dc.description.keywords en_US
pubs.embargo.period Not known
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 Computing and Communications
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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