Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition

Autores
Capello, D.; Martínez, César Ernesto; Milone, Diego Humberto; Stegmayer, Georgina
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classication task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training samples using principal components analysis.In the classification phase, an input face is projected to the obtained eigenspace and classified by an appropriate classifier. Neural network classifiers based on multilayer perceptron models have proven to be well suited to this task. This paper presents an array of multilayer perceptron neural networks trained with a novel no-class resampling strategy which takes into account the balance problem between class and no-class examples andincreases the generalization capabilities. The proposed model is compared against a classical multilayer perceptron classifier for face recognition over the AT&T database of faces, obtaining results that show an improvement over the classification rates of a classical classifier.
Fil: Capello, D.. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; Argentina
Fil: Martínez, César Ernesto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
Fil: Milone, Diego Humberto. Universidad Nacional de Entre Ríos; Argentina
Fil: Stegmayer, Georgina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
Materia
Multilayer Perceptron array
No-class Resampling training algorithm
Face Recognition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/104790

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spelling Array of Multilayer Perceptrons with No-class Resampling Training for Face RecognitionCapello, D.Martínez, César ErnestoMilone, Diego HumbertoStegmayer, GeorginaMultilayer Perceptron arrayNo-class Resampling training algorithmFace Recognitionhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classication task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training samples using principal components analysis.In the classification phase, an input face is projected to the obtained eigenspace and classified by an appropriate classifier. Neural network classifiers based on multilayer perceptron models have proven to be well suited to this task. This paper presents an array of multilayer perceptron neural networks trained with a novel no-class resampling strategy which takes into account the balance problem between class and no-class examples andincreases the generalization capabilities. The proposed model is compared against a classical multilayer perceptron classifier for face recognition over the AT&T database of faces, obtaining results that show an improvement over the classification rates of a classical classifier.Fil: Capello, D.. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; ArgentinaFil: Martínez, César Ernesto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Milone, Diego Humberto. Universidad Nacional de Entre Ríos; ArgentinaFil: Stegmayer, Georgina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaAEPIA2009-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/104790Capello, D.; Martínez, César Ernesto; Milone, Diego Humberto; Stegmayer, Georgina; Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition; AEPIA; Inteligencia Artificial; 3; 44; 12-2009; 5-131137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:05Zoai:ri.conicet.gov.ar:11336/104790instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:05.729CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
title Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
spellingShingle Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
Capello, D.
Multilayer Perceptron array
No-class Resampling training algorithm
Face Recognition
title_short Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
title_full Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
title_fullStr Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
title_full_unstemmed Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
title_sort Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
dc.creator.none.fl_str_mv Capello, D.
Martínez, César Ernesto
Milone, Diego Humberto
Stegmayer, Georgina
author Capello, D.
author_facet Capello, D.
Martínez, César Ernesto
Milone, Diego Humberto
Stegmayer, Georgina
author_role author
author2 Martínez, César Ernesto
Milone, Diego Humberto
Stegmayer, Georgina
author2_role author
author
author
dc.subject.none.fl_str_mv Multilayer Perceptron array
No-class Resampling training algorithm
Face Recognition
topic Multilayer Perceptron array
No-class Resampling training algorithm
Face Recognition
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classication task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training samples using principal components analysis.In the classification phase, an input face is projected to the obtained eigenspace and classified by an appropriate classifier. Neural network classifiers based on multilayer perceptron models have proven to be well suited to this task. This paper presents an array of multilayer perceptron neural networks trained with a novel no-class resampling strategy which takes into account the balance problem between class and no-class examples andincreases the generalization capabilities. The proposed model is compared against a classical multilayer perceptron classifier for face recognition over the AT&T database of faces, obtaining results that show an improvement over the classification rates of a classical classifier.
Fil: Capello, D.. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; Argentina
Fil: Martínez, César Ernesto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
Fil: Milone, Diego Humberto. Universidad Nacional de Entre Ríos; Argentina
Fil: Stegmayer, Georgina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
description A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classication task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training samples using principal components analysis.In the classification phase, an input face is projected to the obtained eigenspace and classified by an appropriate classifier. Neural network classifiers based on multilayer perceptron models have proven to be well suited to this task. This paper presents an array of multilayer perceptron neural networks trained with a novel no-class resampling strategy which takes into account the balance problem between class and no-class examples andincreases the generalization capabilities. The proposed model is compared against a classical multilayer perceptron classifier for face recognition over the AT&T database of faces, obtaining results that show an improvement over the classification rates of a classical classifier.
publishDate 2009
dc.date.none.fl_str_mv 2009-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/104790
Capello, D.; Martínez, César Ernesto; Milone, Diego Humberto; Stegmayer, Georgina; Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition; AEPIA; Inteligencia Artificial; 3; 44; 12-2009; 5-13
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/104790
identifier_str_mv Capello, D.; Martínez, César Ernesto; Milone, Diego Humberto; Stegmayer, Georgina; Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition; AEPIA; Inteligencia Artificial; 3; 44; 12-2009; 5-13
1137-3601
1988-3064
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv AEPIA
publisher.none.fl_str_mv AEPIA
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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