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Authordc.contributor.authorCament Riveros, Leonardo 
Authordc.contributor.authorCastillo, Luis E. es_CL
Authordc.contributor.authorPérez, Juan E. es_CL
Authordc.contributor.authorGaldámes, Francisco J. es_CL
Authordc.contributor.authorPérez Flores, Claudio es_CL
Admission datedc.date.accessioned2014-12-30T20:30:21Z
Available datedc.date.available2014-12-30T20:30:21Z
Publication datedc.date.issued2014
Cita de ítemdc.identifier.citationPattern Recognition Volume 47, Issue 2, February 2014, Pages 568–577en_US
Identifierdc.identifier.otherDOI: 10.1016/j.patcog.2013.09.003
Identifierdc.identifier.urihttp://repositorio.uchile.cl/handle/2250/126867
General notedc.descriptionArtículo de publicación ISIen_US
Abstractdc.description.abstractFace recognition is one of the most extensively studied topics in image analysis because of its wide range of possible applications such as in surveillance, access control, content-based video search, human–computer interaction, electronic advertisement and more. Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database. In this work we propose two new methods for face identification. The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features. The second fuses the entropy-like weighted Gabor features at the score level with the local binary pattern (LBP) applied to the magnitude (LGBP) and phase (LGXP) components of the Gabor features. We used the FERET, AR, and FRGC 2.0 databases to test and compare our results with those previously published. Results on these databases show significant improvement relative to previously published results, reaching the best performance on the FERET and AR databases. Our methods also showed significant robustness to slight pose variations. We tested the proposed methods assuming noisy eye detection to check their robustness to inexact face alignment. Results show that the proposed methods are robust to errors of up to 3 pixels in eye detection.en_US
Patrocinadordc.description.sponsorshipThis research was funded in part by FONDECYT1120613, FONDEFD08I-1060 and the Department of Electrical Engineering, University of Chile (Universidad de Chile).en_US
Lenguagedc.language.isoenen_US
Publisherdc.publisherElsevieren_US
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Keywordsdc.subjectFace recognitionen_US
Títulodc.titleFusion of local normalization and Gabor entropy weighted features for face identificationen_US
Document typedc.typeArtículo de revistaen_US


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile