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dc.contributor.authorLI Wen-shuzh_CN
dc.contributor.author李文书zh_CN
dc.contributor.authorZHOU Chang-le
dc.contributor.author周昌乐
dc.contributor.authorXU Jia-tuo
dc.contributor.author许家佗
dc.date.accessioned2011-04-26T08:23:10Z
dc.date.available2011-04-26T08:23:10Z
dc.date.issued2005zh_CN
dc.identifier.citationZhejiang Univ SCI, 2005 6A(5):454-459zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/8204
dc.description.abstractA novel combined personalized feature Framework is proposed for face recognition (FR). In the framework, the proposed linear discriminant analysis (LDA) makes use of the null space of the within-class scatter matrix effectively, and Global feature vectors (PCA-transformed) and local feature vectors (Gabor wavelet-transformed) are integrated by complex vectors as input feature of improved LDA. The proposed method is compared to other commonly used FR methods on two face databases (ORL and UMIST). Results demonstrated that the performance of the proposed method is superior to that of traditional FR approacheszh_CN
dc.description.sponsorshipthe National Natural ScienceFoundation of China (No. 60275023)zh_CN
dc.language.isoenzh_CN
dc.publisherZhejiang Univ SCIzh_CN
dc.subjectFisher discriminant criterionzh_CN
dc.subjectFace recognition (FR)zh_CN
dc.subjectLinear discriminant analysis (LDA)zh_CN
dc.subjectPrincipal component analysis (PCA)zh_CN
dc.subjectSmall sample size (SSS)zh_CN
dc.titleA novel face recognition method with feature combinationzh_CN
dc.typeArticlezh_CN


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