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dc.contributor.authorVillegas Quezada, Carlos
dc.contributor.authorCliment Vilaró, Joan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2011-07-20T09:32:34Z
dc.date.available2011-07-20T09:32:34Z
dc.date.created2008
dc.date.issued2008
dc.identifier.citationVillegas, C.; Climent, J. Holistic face recognition using multivariate approximation, genetic algorithms and AdaBoost classifier: preliminary results. A: World Congress on Science, Engineering and Technology. "World Congress on Science, Engineering and Technology, WCSET 2008". Venice: 2008, p. 802-806.
dc.identifier.isbn2070-3740
dc.identifier.urihttp://hdl.handle.net/2117/13013
dc.description.abstractSeveral works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p” variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ − Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F’s polynomials to each of the Fi’s polynomials. The winner is the polynomial family closer to F’s corresponding to target face in data base.
dc.format.extent5 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshHuman face recognition (Computer science)
dc.subject.otherAdaBoost classifier
dc.subject.otherHolistic face recognition
dc.subject.otherMinimax multivariate approximation
dc.subject.otherGenetic algorithm
dc.titleHolistic face recognition using multivariate approximation, genetic algorithms and AdaBoost classifier: preliminary results
dc.typeConference report
dc.subject.lemacReconeixement facial (Informàtica)
dc.subject.lemacAlgorismes genètics
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
dc.relation.publisherversionhttp://www.waset.org/programs/Venice08.pdf
dc.rights.accessOpen Access
drac.iddocument3238735
dc.description.versionPostprint (published version)
upcommons.citation.authorVillegas, C.; Climent, J.
upcommons.citation.contributorWorld Congress on Science, Engineering and Technology
upcommons.citation.pubplaceVenice
upcommons.citation.publishedtrue
upcommons.citation.publicationNameWorld Congress on Science, Engineering and Technology, WCSET 2008
upcommons.citation.startingPage802
upcommons.citation.endingPage806
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