Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42502
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dc.contributor.authorCastrillón Santana, Modesto Fernandoen_US
dc.contributor.authorLorenzo Navarro, José Javieren_US
dc.contributor.authorDe Ramón Balmaseda, Enrique Joséen_US
dc.date.accessioned2018-11-19T11:37:55Z-
dc.date.available2018-11-19T11:37:55Z-
dc.date.issued2016en_US
dc.identifier.issn0167-8655en_US
dc.identifier.urihttp://hdl.handle.net/10553/42502-
dc.description.abstractThe periocular area is a reliable cue for automatic gender classification (GC).Each local descriptor and grid configuration report different GC accuracy.The score level fusion of local descriptors increases GC performance.Tests carried out in a challenging large and unrestricted dataset.The fusion of periocular and facial GC reduces the classification error in roughly 20%. Display Omitted Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem. As expected, the achieved results are slightly worse, roughly 8 percentage points lower, than those obtained by state-of-the-art facial GC, but they suggest the validity of the periocular area particularly in difficult scenarios where the whole face is not visible, or has been altered. A final experiment combines in a multi-scale approach features extracted from the periocular, face and head and shoulders areas, fusing them in a two stage ensemble of classifiers. The accuracy reported beats any previous results on the difficult The Images of Groups dataset, reaching 92.46%, with a GC error reduction of almost 20% compared to the best face based GC results in the literature.en_US
dc.languageengen_US
dc.relation.ispartofPattern Recognition Lettersen_US
dc.sourcePattern Recognition Letters [ISSN 0167-8655], v. 82, p. 181-189en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject2405 Biometríaen_US
dc.subject.otherFacial analysisen_US
dc.subject.otherGender recognitionen_US
dc.subject.otherPeriocularen_US
dc.subject.otherSoft biometricsen_US
dc.titleOn using periocular biometric for gender classification in the wilden_US
dc.typeinfo:eu-repo/semantics/articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.patrec.2015.09.014en_US
dc.identifier.scopus2-s2.0-84953274089-
dc.description.lastpage189-
dc.description.firstpage181-
dc.relation.volume82-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
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, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-8673-2725-
crisitem.author.orcid0000-0002-2834-2067-
crisitem.author.orcid0000-0002-2768-1729-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameCastrillón Santana, Modesto Fernando-
crisitem.author.fullNameLorenzo Navarro, José Javier-
crisitem.author.fullNameDe Ramón Balmaseda, Enrique José-
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