A Proposal to increase by genetic algorithm the discriminatory

Two of the most widely used techniques in the field of face recognition with infrared images are PCA (Main Component Analyzes) and LDA (Linear Discriminant Analysis). This paper presents the results obtained by using genetic algorithms to increase the discriminant power of the vectors that make up t...

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Autores:
Martínez, Dúber
Loaiza C, Humberto
Caicedo B, Eduardo
Tipo de recurso:
Fecha de publicación:
2011
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
spa
OAI Identifier:
oai:repository.eafit.edu.co:10784/14473
Acceso en línea:
http://hdl.handle.net/10784/14473
Palabra clave:
Face Recognition
Infrared Images
Genetic Algorithms
Principal Component Analysis
Linear Discriminant Analysis
Reconocimiento De Rostros
Imágenes Infrarrojas
Algoritmos Genéticos
Análisis De Componentes Principales
Análisis Discriminante Lineal
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License
Copyright (c) 2011 Dúber Martínez, Humberto Loaiza C, Eduardo Caicedo B
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spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2011-06-012019-11-22T18:55:38Z2011-06-012019-11-22T18:55:38Z2256-43141794-9165http://hdl.handle.net/10784/14473Two of the most widely used techniques in the field of face recognition with infrared images are PCA (Main Component Analyzes) and LDA (Linear Discriminant Analysis). This paper presents the results obtained by using genetic algorithms to increase the discriminant power of the vectors that make up the space of characteristics generated by these techniques, by means of the weighted allocation of weights to each vector according to their level of contribution in the stage of classification. It is shown that under the proposed scheme, a lower classification error is obtained with respect to the conventional method.Dos de las técnicas más ampliamente utilizadas en el campo del reconocimiento de rostros con imágenes infrarrojas son PCA (Principal Component Analisys) y LDA (Linear Discriminant Analysis). En este trabajo se presentan los resultados obtenidos al emplear algoritmos genéticos para incrementar el poder discriminante de los vectores que conforman el espacio de características generado por dichas técnicas, por medio de la asignación ponderada de pesos a cada vector según su nivel de aporte en la etapa de clasificación. Se muestra que bajo el esquema propuesto, se obtiene un menor error de clasificación respecto al método convencional.application/pdfspaUniversidad EAFIThttp://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/403http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/403Copyright (c) 2011 Dúber Martínez, Humberto Loaiza C, Eduardo Caicedo BAcceso abiertohttp://purl.org/coar/access_right/c_abf2instname:Universidad EAFITreponame:Repositorio Institucional Universidad EAFITIngeniería y Ciencia; Vol 7, No 13 (2011)A Proposal to increase by genetic algorithm the discriminatoryUna propuesta para incrementar la capacidad discriminante de las técnicas PCA y LDA aplicadas al reconocimiento de rostros con imágenes IRarticleinfo:eu-repo/semantics/articlepublishedVersioninfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Face RecognitionInfrared ImagesGenetic AlgorithmsPrincipal Component AnalysisLinear Discriminant AnalysisReconocimiento De RostrosImágenes InfrarrojasAlgoritmos GenéticosAnálisis De Componentes PrincipalesAnálisis Discriminante LinealMartínez, Dúberdd59f8c8-e405-4a41-8d14-5217954bc97c-1Loaiza C, Humberto38e5ad76-c6d2-4705-8fe3-57d5821d3d9c-1Caicedo B, Eduardoe66b5adf-c7ae-4424-a343-79b3241e023f-1Universidad del ValleIngeniería y Ciencia713111130ing.cienc.THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/8be39da9-2450-4343-b7ee-e4357838151d/downloadda9b21a5c7e00c7f1127cef8e97035e0MD51ORIGINAL6.pdf6.pdfTexto completo PDFapplication/pdf311040https://repository.eafit.edu.co/bitstreams/4137b136-18ce-4ada-8173-a77726e6bf2f/download4060c6835b3e677aff97ea414ffd60e4MD52articulo.htmlarticulo.htmlTexto completo HTMLtext/html373https://repository.eafit.edu.co/bitstreams/4d1151b4-074d-44a3-b28f-b3b0ae0539e2/download5f732d0fb8ff342e72ca9370055bbd0fMD5310784/14473oai:repository.eafit.edu.co:10784/144732024-12-04 11:47:08.212open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv A Proposal to increase by genetic algorithm the discriminatory
dc.title.spa.fl_str_mv Una propuesta para incrementar la capacidad discriminante de las técnicas PCA y LDA aplicadas al reconocimiento de rostros con imágenes IR
title A Proposal to increase by genetic algorithm the discriminatory
spellingShingle A Proposal to increase by genetic algorithm the discriminatory
Face Recognition
Infrared Images
Genetic Algorithms
Principal Component Analysis
Linear Discriminant Analysis
Reconocimiento De Rostros
Imágenes Infrarrojas
Algoritmos Genéticos
Análisis De Componentes Principales
Análisis Discriminante Lineal
title_short A Proposal to increase by genetic algorithm the discriminatory
title_full A Proposal to increase by genetic algorithm the discriminatory
title_fullStr A Proposal to increase by genetic algorithm the discriminatory
title_full_unstemmed A Proposal to increase by genetic algorithm the discriminatory
title_sort A Proposal to increase by genetic algorithm the discriminatory
dc.creator.fl_str_mv Martínez, Dúber
Loaiza C, Humberto
Caicedo B, Eduardo
dc.contributor.author.spa.fl_str_mv Martínez, Dúber
Loaiza C, Humberto
Caicedo B, Eduardo
dc.contributor.affiliation.spa.fl_str_mv Universidad del Valle
dc.subject.keyword.eng.fl_str_mv Face Recognition
Infrared Images
Genetic Algorithms
Principal Component Analysis
Linear Discriminant Analysis
topic Face Recognition
Infrared Images
Genetic Algorithms
Principal Component Analysis
Linear Discriminant Analysis
Reconocimiento De Rostros
Imágenes Infrarrojas
Algoritmos Genéticos
Análisis De Componentes Principales
Análisis Discriminante Lineal
dc.subject.keyword.spa.fl_str_mv Reconocimiento De Rostros
Imágenes Infrarrojas
Algoritmos Genéticos
Análisis De Componentes Principales
Análisis Discriminante Lineal
description Two of the most widely used techniques in the field of face recognition with infrared images are PCA (Main Component Analyzes) and LDA (Linear Discriminant Analysis). This paper presents the results obtained by using genetic algorithms to increase the discriminant power of the vectors that make up the space of characteristics generated by these techniques, by means of the weighted allocation of weights to each vector according to their level of contribution in the stage of classification. It is shown that under the proposed scheme, a lower classification error is obtained with respect to the conventional method.
publishDate 2011
dc.date.issued.none.fl_str_mv 2011-06-01
dc.date.available.none.fl_str_mv 2019-11-22T18:55:38Z
dc.date.accessioned.none.fl_str_mv 2019-11-22T18:55:38Z
dc.date.none.fl_str_mv 2011-06-01
dc.type.eng.fl_str_mv article
info:eu-repo/semantics/article
publishedVersion
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http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.local.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 2256-4314
1794-9165
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/14473
identifier_str_mv 2256-4314
1794-9165
url http://hdl.handle.net/10784/14473
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.isversionof.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/403
dc.relation.uri.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/403
dc.rights.eng.fl_str_mv Copyright (c) 2011 Dúber Martínez, Humberto Loaiza C, Eduardo Caicedo B
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Copyright (c) 2011 Dúber Martínez, Humberto Loaiza C, Eduardo Caicedo B
Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.eng.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.source.none.fl_str_mv instname:Universidad EAFIT
reponame:Repositorio Institucional Universidad EAFIT
dc.source.spa.fl_str_mv Ingeniería y Ciencia; Vol 7, No 13 (2011)
instname_str Universidad EAFIT
institution Universidad EAFIT
reponame_str Repositorio Institucional Universidad EAFIT
collection Repositorio Institucional Universidad EAFIT
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