Proposing the novelty classifier for face recognition

Detalhes bibliográficos
Autor(a) principal: Costa Filho,Cicero Ferreira Fernandes
Data de Publicação: 2014
Outros Autores: Falcão,Thiago de Azevedo, Costa,Marly Guimarães Fernandes, Pereira,José Raimundo Gomes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Engenharia Biomédica (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-31512014000400003
Resumo: INTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.
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spelling Proposing the novelty classifier for face recognitionFace recognitionNovelty classifierK Nearest NeighborPrincipal Component AnalysisINTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.SBEB - Sociedade Brasileira de Engenharia Biomédica2014-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-31512014000400003Revista Brasileira de Engenharia Biomédica v.30 n.4 2014reponame:Revista Brasileira de Engenharia Biomédica (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/1517-3151.0543info:eu-repo/semantics/openAccessCosta Filho,Cicero Ferreira FernandesFalcão,Thiago de AzevedoCosta,Marly Guimarães FernandesPereira,José Raimundo Gomeseng2015-01-15T00:00:00Zoai:scielo:S1517-31512014000400003Revistahttp://www.scielo.br/rbebONGhttps://old.scielo.br/oai/scielo-oai.php||rbeb@rbeb.org.br1984-77421517-3151opendoar:2015-01-15T00:00Revista Brasileira de Engenharia Biomédica (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Proposing the novelty classifier for face recognition
title Proposing the novelty classifier for face recognition
spellingShingle Proposing the novelty classifier for face recognition
Costa Filho,Cicero Ferreira Fernandes
Face recognition
Novelty classifier
K Nearest Neighbor
Principal Component Analysis
title_short Proposing the novelty classifier for face recognition
title_full Proposing the novelty classifier for face recognition
title_fullStr Proposing the novelty classifier for face recognition
title_full_unstemmed Proposing the novelty classifier for face recognition
title_sort Proposing the novelty classifier for face recognition
author Costa Filho,Cicero Ferreira Fernandes
author_facet Costa Filho,Cicero Ferreira Fernandes
Falcão,Thiago de Azevedo
Costa,Marly Guimarães Fernandes
Pereira,José Raimundo Gomes
author_role author
author2 Falcão,Thiago de Azevedo
Costa,Marly Guimarães Fernandes
Pereira,José Raimundo Gomes
author2_role author
author
author
dc.contributor.author.fl_str_mv Costa Filho,Cicero Ferreira Fernandes
Falcão,Thiago de Azevedo
Costa,Marly Guimarães Fernandes
Pereira,José Raimundo Gomes
dc.subject.por.fl_str_mv Face recognition
Novelty classifier
K Nearest Neighbor
Principal Component Analysis
topic Face recognition
Novelty classifier
K Nearest Neighbor
Principal Component Analysis
description INTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.
publishDate 2014
dc.date.none.fl_str_mv 2014-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-31512014000400003
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1517-3151.0543
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dc.publisher.none.fl_str_mv SBEB - Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv SBEB - Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Biomédica v.30 n.4 2014
reponame:Revista Brasileira de Engenharia Biomédica (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
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instname_str Sociedade Brasileira de Engenharia Biomédica (SBEB)
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reponame_str Revista Brasileira de Engenharia Biomédica (Online)
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repository.name.fl_str_mv Revista Brasileira de Engenharia Biomédica (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)
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