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On reducing the effect of silhouette quality on individual gait recognition: a feature fusion approach

dc.contributor.authorJia, Ning
dc.contributor.authorSanchez, Victor
dc.contributor.authorLi, Chang-Tsun
dc.contributor.authorMansour, Hassan
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2017-06-30T08:19:17Z
dc.date.available2017-06-30T08:19:17Z
dc.date.issued2015
dc.description.abstractThe quality of the extracted gait silhouettes can hinder the performance and practicability of gait recognition algorithms. In this paper, we propose a framework that integrates a feature fusion approach to improve recognition rate under this situation. Specifically, we first generate a dataset containing gait silhouettes with various qualities based on the CASIA Dataset B. We then fuse gallery data with different qualities and project data into embedded subspaces. We perform classification based on the Euclidean distances between fused gallery features and probe features. Experimental results show that the proposed framework can provide important improvements on recognition rate.en
dc.identifier.isbn978-3-88579-639-8
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2015
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-245
dc.titleOn reducing the effect of silhouette quality on individual gait recognition: a feature fusion approachen
dc.typeText/Conference Paper
gi.citation.endPage60
gi.citation.publisherPlaceBonn
gi.citation.startPage49
gi.conference.date9.-11. September 2015
gi.conference.locationDarmstadt

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