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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/104790
Ver los metadatos del registro completo
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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/ |
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application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
AEPIA |
publisher.none.fl_str_mv |
AEPIA |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1842269263818129408 |
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13.13397 |