A kernel maximum uncertainty discriminant analysis and its application to face recognition

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dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorGIRALDI, G. A.
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2023-08-26T23:50:05Z
dc.date.available2023-08-26T23:50:05Z
dc.date.issued2009-02-05
dc.description.abstractIn this paper, we extend the Maximum uncertainty Linear Discriminant Analysis (MLDA), proposed recently for limited sample size problems, to its kernel version. The new Kernel Maximum uncertainty Discriminant Analysis (KMDA) is a two-stage method composed of Kernel Principal Component Analysis (KPCA) followed by the standard MLDA. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other existing kernel discriminant methods, such as Generalized Discriminant Analysis (GDA) and Regularized Kernel Discriminant Analysis (RKDA). The classification results indicate that KMDA performs as well as GDA and RKDA, with the advantage of being a straightforward stabilization approach for the within-class scatter matrix that uses higher-order features for further classification improvements.
dc.description.firstpage341
dc.description.lastpage346
dc.description.volume1
dc.identifier.citationTHOMAZ, C. E.; GIRALDI, G. A. A kernel maximum uncertainty discriminant analysis and its application to face recognition. VISAPP 2009 - Proceedings of the 4th International Conference on Computer Vision Theory and Applications, v. 1, p. 341-346, feb. 2009.
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/5013
dc.relation.ispartofVISAPP 2009 - Proceedings of the 4th International Conference on Computer Vision Theory and Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageFace recognition
dc.subject.otherlanguageLimited sample size problems
dc.subject.otherlanguageNon-linear discriminant analysis
dc.titleA kernel maximum uncertainty discriminant analysis and its application to face recognition
dc.typeArtigo de evento
fei.scopus.citations2
fei.scopus.eid2-s2.0-70349680786
fei.scopus.subjectClassification results
fei.scopus.subjectFace database
fei.scopus.subjectGeneralized discriminant analysis
fei.scopus.subjectHigher order
fei.scopus.subjectKernel discriminant analysis
fei.scopus.subjectKernel principal component analysis
fei.scopus.subjectLimited sample size problems
fei.scopus.subjectLinear discriminant analysis
fei.scopus.subjectNon-linear discriminant analysis
fei.scopus.subjectSample size problems
fei.scopus.subjectTwo-stage methods
fei.scopus.subjectWithin-class scatter matrix
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=70349680786&origin=inward
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