A maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition

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dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorKitani E.C.
dc.contributor.authorGillies D.F.
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T22:05:40Z
dc.date.available2022-01-12T22:05:40Z
dc.date.issued2006-01-01
dc.description.abstract© 2007, The Brazilian Computer Society.A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or pre-processed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this study, a new LDA-based method is proposed. It is based on a straightforward stabilisation approach for the within-class scatter matrix. 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 LDA-based methods. The classification results indicate that our method improves the LDA classification performance when the within-class scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features. Since statistical discrimination methods are suitable not only for classification but also for characterisation of differences between groups of patterns, further experiments were carried out in order to extend the new LDA-based method to visually analyse the most discriminating hyper-plane separating two populations. The additional results based on frontal face images indicate that the new LDA-based mapping provides an intuitive interpretation of the two-group classification tasks performed, highlighting the group differences captured by the multivariate statistical approach proposed.
dc.description.firstpage7
dc.description.issuenumber2
dc.description.lastpage18
dc.description.volume12
dc.identifier.citationTHOMAZ, C. E.; KITANI, E. C.; GILLES, E. C. A maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition. Journal of the Brazilian Computer Society, v. 12, n. 2, p. 7-18,
dc.identifier.doi10.1007/BF03192391
dc.identifier.issn1678-4804
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4355
dc.relation.ispartofJournal of the Brazilian Computer Society
dc.rightsAcesso Aberto
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative commons (CC BY-NC 4.0). Fonte: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=37449027439&origin=inward. Acesso em: 25 agosto 2022.
dc.subject.otherlanguageface recognition
dc.subject.otherlanguageLinear Discriminant Analysis (LDA)
dc.subject.otherlanguagesample size
dc.subject.otherlanguagesmall
dc.titleA maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition
dc.typeArtigo
fei.scopus.citations51
fei.scopus.eid2-s2.0-37449027439
fei.scopus.subjectClassification performance
fei.scopus.subjectClassification results
fei.scopus.subjectGroup classification
fei.scopus.subjectLinear discriminant analysis
fei.scopus.subjectMultivariate statistical approaches
fei.scopus.subjectSample sizes
fei.scopus.subjectsmall
fei.scopus.subjectWithin-class scatter matrix
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=37449027439&origin=inward
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