Proposing the novelty classifier for face recognition
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
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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Revista Brasileira de Engenharia Biomédica (Online) |
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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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-31512014000400003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-31512014000400003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1517-3151.0543 |
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 |
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) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Revista Brasileira de Engenharia Biomédica (Online) |
collection |
Revista Brasileira de Engenharia Biomédica (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Engenharia Biomédica (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbeb@rbeb.org.br |
_version_ |
1754820915144687616 |