Feature selection for face recognition based on multi-objective evolutionary wrappers

Autores
Vignolo, Leandro Daniel; Milone, Diego Humberto; Scharcanski, Jacob
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
Fil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Scharcanski, Jacob. Universidade Federal do Rio Grande do Sul. Instituto de Informatica and Dept. de Engenharia Eletrica; Brasil
Materia
Wrappers
Multi-Objective Genetic Algorithms
Feature Selection
Face Recognition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14573
Acceder
id CONICETDig_54cb0607438fab172301eb0253be09d2
oai_identifier_str oai:ri.conicet.gov.ar:11336/14573
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Feature selection for face recognition based on multi-objective evolutionary wrappersVignolo, Leandro DanielMilone, Diego HumbertoScharcanski, JacobWrappersMulti-Objective Genetic AlgorithmsFeature SelectionFace Recognitionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.Fil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; ArgentinaFil: Scharcanski, Jacob. Universidade Federal do Rio Grande do Sul. Instituto de Informatica and Dept. de Engenharia Eletrica; BrasilElsevier2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14573Vignolo, Leandro Daniel; Milone, Diego Humberto; Scharcanski, Jacob; Feature selection for face recognition based on multi-objective evolutionary wrappers; Elsevier; Expert Systems With Applications; 40; 13; 10-2013; 5077-50840957-4174enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2013.03.032info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417413001954info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:41Zoai:ri.conicet.gov.ar:11336/14573instacron: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:34982025-09-29 09:33:42.058CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Feature selection for face recognition based on multi-objective evolutionary wrappers
title Feature selection for face recognition based on multi-objective evolutionary wrappers
spellingShingle Feature selection for face recognition based on multi-objective evolutionary wrappers
Vignolo, Leandro Daniel
Wrappers
Multi-Objective Genetic Algorithms
Feature Selection
Face Recognition
title_short Feature selection for face recognition based on multi-objective evolutionary wrappers
title_full Feature selection for face recognition based on multi-objective evolutionary wrappers
title_fullStr Feature selection for face recognition based on multi-objective evolutionary wrappers
title_full_unstemmed Feature selection for face recognition based on multi-objective evolutionary wrappers
title_sort Feature selection for face recognition based on multi-objective evolutionary wrappers
dc.creator.none.fl_str_mv Vignolo, Leandro Daniel
Milone, Diego Humberto
Scharcanski, Jacob
author Vignolo, Leandro Daniel
author_facet Vignolo, Leandro Daniel
Milone, Diego Humberto
Scharcanski, Jacob
author_role author
author2 Milone, Diego Humberto
Scharcanski, Jacob
author2_role author
author
dc.subject.none.fl_str_mv Wrappers
Multi-Objective Genetic Algorithms
Feature Selection
Face Recognition
topic Wrappers
Multi-Objective Genetic Algorithms
Feature Selection
Face Recognition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
Fil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Scharcanski, Jacob. Universidade Federal do Rio Grande do Sul. Instituto de Informatica and Dept. de Engenharia Eletrica; Brasil
description Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/14573
Vignolo, Leandro Daniel; Milone, Diego Humberto; Scharcanski, Jacob; Feature selection for face recognition based on multi-objective evolutionary wrappers; Elsevier; Expert Systems With Applications; 40; 13; 10-2013; 5077-5084
0957-4174
url http://hdl.handle.net/11336/14573
identifier_str_mv Vignolo, Leandro Daniel; Milone, Diego Humberto; Scharcanski, Jacob; Feature selection for face recognition based on multi-objective evolutionary wrappers; Elsevier; Expert Systems With Applications; 40; 13; 10-2013; 5077-5084
0957-4174
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2013.03.032
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417413001954
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1844613036891963392
score 13.070432

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