Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9884
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLi, R-
dc.creatorChan, CL-
dc.creatorEddie,-
dc.creatorBaciu, G-
dc.date.accessioned2015-09-30T09:43:53Z-
dc.date.available2015-09-30T09:43:53Z-
dc.identifier.isbn978-1-4244-8041-8-
dc.identifier.urihttp://hdl.handle.net/10397/9884-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDirect LDAen_US
dc.subjectFace Recognitionen_US
dc.subjectLDA/QRen_US
dc.subjectPseudo-inverse LDAen_US
dc.titleDLDA and LDA/QR equivalence framework for human face recognitionen_US
dc.typeConference Paperen_US
dc.identifier.spage180-
dc.identifier.epage185-
dc.identifier.doi10.1109/COGINF.2010.5599743-
dcterms.abstractSingularity problem in human face feature extraction is very challenging that has gained a lot of attentions during the last decade. A pseudo-inverse linear discriminant analysis (LDA) plays a important role to solve the singularity problem of the scatter matrices. In this paper, we make use of Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant Analysis (DLDA) to solve the singularity problem in face feature recognition. We also show that an equivalent relationship between DLDA and LDA/QR. They can be regarded as a special case of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA could be a two-stage LDA method. Interestingly, we find that the first stage of DLDA can act as a dimensionality reduction algorithm. In our experiment, we compare DLDA and LDA/QR algorithms in terms of classification accuracy, computational complexity in ORL and Yale face datasets. We have also conducted experiments to compare their first stages on these datasets. Our results indicate that the empirical and theoretic proofs of equivalence between DLDA and LDA/QR algorithms coincidentally converge and verify their same capabilities in the dimension reduction.-
dcterms.bibliographicCitation2010 9th IEEE International Conference on Cognitive Informatics (ICCI), 7-9 July 2010, Beijing, p. 180-185-
dcterms.issued2010-
dc.relation.ispartofbook2010 9th IEEE International Conference on Cognitive Informatics (ICCI), 7-9 July 2010, Beijing-
dc.relation.conferenceIEEE International Conference on Cognitive Informatics [ICCI]-
dc.identifier.rosgroupidr53900-
dc.description.ros2010-2011 > Academic research: refereed > Refereed conference paper-
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