A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of the Brazilian Computer Society |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002006000300002 |
Resumo: | 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. |
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Journal of the Brazilian Computer Society |
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A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognitionLinear Discriminant Analysis (LDA)small sample sizeface recognitionA 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.Sociedade Brasileira de Computação2006-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002006000300002Journal of the Brazilian Computer Society v.12 n.2 2006reponame:Journal of the Brazilian Computer Societyinstname:Sociedade Brasileira de Computação (SBC)instacron:UFRGS10.1007/BF03192390info:eu-repo/semantics/openAccessThomaz,Carlos EduardoKitani,Edson CaoruGillies,Duncan Fyfeeng2010-05-24T00:00:00Zoai:scielo:S0104-65002006000300002Revistahttps://journal-bcs.springeropen.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpjbcs@icmc.sc.usp.br1678-48040104-6500opendoar:2010-05-24T00:00Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC)false |
dc.title.none.fl_str_mv |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
title |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
spellingShingle |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition Thomaz,Carlos Eduardo Linear Discriminant Analysis (LDA) small sample size face recognition |
title_short |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
title_full |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
title_fullStr |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
title_full_unstemmed |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
title_sort |
A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition |
author |
Thomaz,Carlos Eduardo |
author_facet |
Thomaz,Carlos Eduardo Kitani,Edson Caoru Gillies,Duncan Fyfe |
author_role |
author |
author2 |
Kitani,Edson Caoru Gillies,Duncan Fyfe |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Thomaz,Carlos Eduardo Kitani,Edson Caoru Gillies,Duncan Fyfe |
dc.subject.por.fl_str_mv |
Linear Discriminant Analysis (LDA) small sample size face recognition |
topic |
Linear Discriminant Analysis (LDA) small sample size face recognition |
description |
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. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002006000300002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002006000300002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/BF03192390 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
dc.source.none.fl_str_mv |
Journal of the Brazilian Computer Society v.12 n.2 2006 reponame:Journal of the Brazilian Computer Society instname:Sociedade Brasileira de Computação (SBC) instacron:UFRGS |
instname_str |
Sociedade Brasileira de Computação (SBC) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Journal of the Brazilian Computer Society |
collection |
Journal of the Brazilian Computer Society |
repository.name.fl_str_mv |
Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC) |
repository.mail.fl_str_mv |
jbcs@icmc.sc.usp.br |
_version_ |
1754734669918633984 |