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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier6qtX3pFwXQZeBBx/GEG3a
Repositorysid.inpe.br/banon/2005/07.05.16.47
Last Update2005:07.05.03.00.00 (UTC) administrator
Metadatasid.inpe.br/banon/2005/07.05.16.47.10
Metadata Last Update2020:02.19.03.19.08 (UTC) administrator
Citation KeyThomazGill:2005:ApFaRe
TitleA maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition
FormatOn-line
Year2005
Access Date2021, Nov. 27
Number of Files1
Size107 KiB
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Author1 Thomaz, Carlos Eduardo
2 Gillies, Duncan Fyfe
Affiliation1 Centro Universitario da FEI, Sao Paulo, Brazil
2 Imperial College, London, UK
EditorRodrigues, Maria Andr?ia Formico
Frery, Alejandro C?sar
e-Mail Addresscet@fei.edu.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:10:58 :: cet -> banon ::
2008-08-26 15:17:01 :: banon -> administrator ::
2009-08-13 20:37:43 :: administrator -> banon ::
2010-08-28 20:01:17 :: banon -> administrator ::
2020-02-19 03:19:08 :: administrator -> :: 2005
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsLDA
maximum uncertainty LDA
limited sample size
face recognition
AbstractA 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 paper, a maximum uncertainty 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 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.
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data URLhttp://urlib.net/ibi/6qtX3pFwXQZeBBx/GEG3a
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/GEG3a
Languageen
Target Filethomazc_mlda.pdf
User Groupcet
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Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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