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Conference Paper: Novel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model features

TitleNovel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model features
Authors
KeywordsContour Projection
Gait Recognition
Principal Component Analysis
Skeleton Model
Support Vector Machine
Issue Date2009
Citation
2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 1-4 How to Cite?
AbstractGait is a potential behavioral feature, and many allied studies have demonstrated that it can be served as a useful biometric feature for recognition. This paper described a novel gait recognition technique based on support vector machine fusion of contour projection and skeleton model features. A principal component analysis method was used to lower the dimension of contour projection after segmenting silhouettes from the background in the key frame of gait picture sequence and a skeleton model was built to produce other shape features. The combining features were fused by a support vector machine and tested on the CASIA database at the feature level and decision level based on posterior probability. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/173419
References

 

DC FieldValueLanguage
dc.contributor.authorMing, Den_US
dc.contributor.authorBai, Yen_US
dc.contributor.authorZhang, Cen_US
dc.contributor.authorWan, Ben_US
dc.contributor.authorHu, Yen_US
dc.contributor.authorLuk, KDKen_US
dc.date.accessioned2012-10-30T06:30:58Z-
dc.date.available2012-10-30T06:30:58Z-
dc.date.issued2009en_US
dc.identifier.citation2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 1-4en_US
dc.identifier.urihttp://hdl.handle.net/10722/173419-
dc.description.abstractGait is a potential behavioral feature, and many allied studies have demonstrated that it can be served as a useful biometric feature for recognition. This paper described a novel gait recognition technique based on support vector machine fusion of contour projection and skeleton model features. A principal component analysis method was used to lower the dimension of contour projection after segmenting silhouettes from the background in the key frame of gait picture sequence and a skeleton model was built to produce other shape features. The combining features were fused by a support vector machine and tested on the CASIA database at the feature level and decision level based on posterior probability. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm. © 2009 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009en_US
dc.subjectContour Projectionen_US
dc.subjectGait Recognitionen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectSkeleton Modelen_US
dc.subjectSupport Vector Machineen_US
dc.titleNovel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model featuresen_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.identifier.authorityLuk, KDK=rp00333en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/CIMSA.2009.5069906en_US
dc.identifier.scopuseid_2-s2.0-77950801418en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77950801418&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage1en_US
dc.identifier.epage4en_US
dc.identifier.scopusauthoridMing, D=9745824400en_US
dc.identifier.scopusauthoridBai, Y=35108689200en_US
dc.identifier.scopusauthoridZhang, C=35110047100en_US
dc.identifier.scopusauthoridWan, B=7102316798en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US
dc.identifier.scopusauthoridLuk, KDK=7201921573en_US

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