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dc.contributor.authorJvmahong, H.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLu, C.
dc.contributor.editorWang, X.
dc.contributor.editorYeung, D. S.
dc.date.accessioned2017-01-30T11:40:11Z
dc.date.available2017-01-30T11:40:11Z
dc.date.created2015-03-03T20:17:37Z
dc.date.issued2012
dc.identifier.citationHuxidan and Liu, W. and Lu, C. 2012. A modularly vectorized two dimensional LDA for face recognition, in International Conference on Machine Learning and Cybernetics (ICMLC), Jul 15-17 2012, pp. 695-701. Xian, Shaanxi, China: Institute of Electrical and Electronics Engineers (IEEE).
dc.identifier.urihttp://hdl.handle.net/20.500.11937/13914
dc.identifier.doi10.1109/ICMLC.2012.6359009
dc.description.abstract

In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for image. Finally 2DLDA is applied directly on these 2D matrices. Experimental results on ORL, Yale B and PIE databases show that the proposed method can achieve better recognition performance in comparison with RLDA, 2DPCA and 2DLDA.

dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleA modularly vectorized two dimensional LDA for face recognition
dc.typeConference Paper
dcterms.source.startPage695
dcterms.source.endPage701
dcterms.source.issn2160133X
dcterms.source.titleProceedings of the 2012 International Conference on Machine Learning and Cybernetics
dcterms.source.seriesProceedings of the 2012 International Conference on Machine Learning and Cybernetics
dcterms.source.conferenceInternational Conference on Machine Learning and Cybernetics (ICMLC 2012)
dcterms.source.conference-start-dateJul 15 2012
dcterms.source.conferencelocationXian, Shaanxi, China
dcterms.source.placeUSA
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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