GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247 |
Resumo: | The dimensionality of existing data make it difficult to deploy any information to identify features that discriminate between the classes of interest. Feature selection involves reducing the number of features, removes irrelevant, noisy and redundant data without significantly decreasing the prediction accuracy of the classifier. An efficient feature selection and classification technique for face recognition is presented in this paper. Genetic Algorithms (GAs) for feature selection and Support Vector Machine (SVM) for classification are incorporated in the proposed technique. The proposed GAs-SVM technique has two purposes in this research: Selecting of the optimal feature subset and Selecting of the kernel parameters for SVM classifier. The input feature vector for the GAs-SVM are extracted by using the Discrete Cosine Transform (DCT). We evaluate its efficiency compared to the recently proposed feature selection algorithm based on mutual information. The results show that the proposed approach is promising, it is able to select small subsets and still improve the classification accuracy. |
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INFOCOMP: Jornal de Ciência da Computação |
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GA-SVM and Mutual Information based Frequency Feature Selection for Face RecognitionFace recognitionFeature SelectionMutual InformationGenetic AlgorithmSupport Vector MachineDiscrete Cosine TransformThe dimensionality of existing data make it difficult to deploy any information to identify features that discriminate between the classes of interest. Feature selection involves reducing the number of features, removes irrelevant, noisy and redundant data without significantly decreasing the prediction accuracy of the classifier. An efficient feature selection and classification technique for face recognition is presented in this paper. Genetic Algorithms (GAs) for feature selection and Support Vector Machine (SVM) for classification are incorporated in the proposed technique. The proposed GAs-SVM technique has two purposes in this research: Selecting of the optimal feature subset and Selecting of the kernel parameters for SVM classifier. The input feature vector for the GAs-SVM are extracted by using the Discrete Cosine Transform (DCT). We evaluate its efficiency compared to the recently proposed feature selection algorithm based on mutual information. The results show that the proposed approach is promising, it is able to select small subsets and still improve the classification accuracy.Editora da UFLA2009-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247INFOCOMP Journal of Computer Science; Vol. 8 No. 1 (2009): March, 2009; 20-291982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247/232Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessAmine, AouatifAkadi, Ali elRziza, MohammedAboutajdine, Driss2015-07-01T12:46:27Zoai:infocomp.dcc.ufla.br:article/247Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:27.138002INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
title |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
spellingShingle |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition Amine, Aouatif Face recognition Feature Selection Mutual Information Genetic Algorithm Support Vector Machine Discrete Cosine Transform |
title_short |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
title_full |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
title_fullStr |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
title_full_unstemmed |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
title_sort |
GA-SVM and Mutual Information based Frequency Feature Selection for Face Recognition |
author |
Amine, Aouatif |
author_facet |
Amine, Aouatif Akadi, Ali el Rziza, Mohammed Aboutajdine, Driss |
author_role |
author |
author2 |
Akadi, Ali el Rziza, Mohammed Aboutajdine, Driss |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Amine, Aouatif Akadi, Ali el Rziza, Mohammed Aboutajdine, Driss |
dc.subject.por.fl_str_mv |
Face recognition Feature Selection Mutual Information Genetic Algorithm Support Vector Machine Discrete Cosine Transform |
topic |
Face recognition Feature Selection Mutual Information Genetic Algorithm Support Vector Machine Discrete Cosine Transform |
description |
The dimensionality of existing data make it difficult to deploy any information to identify features that discriminate between the classes of interest. Feature selection involves reducing the number of features, removes irrelevant, noisy and redundant data without significantly decreasing the prediction accuracy of the classifier. An efficient feature selection and classification technique for face recognition is presented in this paper. Genetic Algorithms (GAs) for feature selection and Support Vector Machine (SVM) for classification are incorporated in the proposed technique. The proposed GAs-SVM technique has two purposes in this research: Selecting of the optimal feature subset and Selecting of the kernel parameters for SVM classifier. The input feature vector for the GAs-SVM are extracted by using the Discrete Cosine Transform (DCT). We evaluate its efficiency compared to the recently proposed feature selection algorithm based on mutual information. The results show that the proposed approach is promising, it is able to select small subsets and still improve the classification accuracy. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/247/232 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 8 No. 1 (2009): March, 2009; 20-29 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740861534208 |