Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/72743
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dc.contributor.authorDéniz Suárez,Oscaren_US
dc.contributor.authorCastrillon, M.en_US
dc.contributor.authorHernández, M.en_US
dc.date.accessioned2020-05-22T17:55:59Z-
dc.date.available2020-05-22T17:55:59Z-
dc.date.issued2003en_US
dc.identifier.issn0167-8655en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/72743-
dc.description.abstractSupport vector machines (SVM) and independent component analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of principal component analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.en_US
dc.languageengen_US
dc.relation.ispartofPattern Recognition Lettersen_US
dc.sourcePattern Recognition Letters [ISSN 0167-8655], v. 24 (13), p. 2153-2157, (Septiembre 2003)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherArtifactsen_US
dc.subject.otherFace recognitionen_US
dc.subject.otherSupport vector machinesen_US
dc.subject.otherIndependent component analysisen_US
dc.titleFace recognition using independent component analysis and support vector machinesen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.relation.conference3rd International Conference on Audia and Video Based Biometric Person Authentication (AVBPA 2001)-
dc.identifier.doi10.1016/S0167-8655(03)00081-3en_US
dc.identifier.scopus17144441960-
dc.identifier.isi000183925400007-
dc.contributor.authorscopusid8562422200-
dc.contributor.authorscopusid22333278500-
dc.contributor.authorscopusid57212239402-
dc.description.lastpage2157en_US
dc.identifier.issue13-
dc.description.firstpage2153en_US
dc.relation.volume24en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid599634-
dc.contributor.daisngid32145428-
dc.contributor.daisngid28403683-
dc.description.numberofpages5en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Deniz, O-
dc.contributor.wosstandardWOS:Castrillon, M-
dc.contributor.wosstandardWOS:Hernandez, M-
dc.date.coverdateEnero 2003en_US
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptSIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptSIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.orcid0000-0002-8673-2725-
crisitem.author.orcid0000-0001-9717-8048-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameDéniz Suárez,Oscar-
crisitem.author.fullNameCastrillón Santana, Modesto Fernando-
crisitem.author.fullNameHernández Tejera, Francisco Mario-
crisitem.author.departamentoInformática y Sistemas-
crisitem.author.departamentoInformática y Sistemas-
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