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Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes
dc.contributor.author | Andreu Cabedo, Yasmina | |
dc.contributor.author | García-Sevilla, Pedro | |
dc.contributor.author | Mollineda, Ramón A. | |
dc.date.accessioned | 2016-01-21T14:40:20Z | |
dc.date.available | 2016-01-21T14:40:20Z | |
dc.date.issued | 2014-01 | |
dc.identifier.citation | ANDREU, Yasmina; GARCÍA-SEVILLA, Pedro; MOLLINEDA, Ramón A. Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes. Image and Vision Computing, 2014, vol. 32, no 1, p. 27-36. | ca_CA |
dc.identifier.issn | 0262-8856 | |
dc.identifier.uri | http://hdl.handle.net/10234/146079 | |
dc.description.abstract | This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA + LDA and SVM) and two performance measures (CCR and d′) is provided over single- and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs. | ca_CA |
dc.format.extent | 25 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Image and Vision Computing, 2014, vol. 32, no 1 | ca_CA |
dc.rights | © 2013 Elsevier B.V. All rights reserved. | ca_CA |
dc.rights | Attribution-NonCommercial-NoDerivs 4.0 Spain | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Face analysis | ca_CA |
dc.subject | Gender classification | ca_CA |
dc.subject | Global/local representation | ca_CA |
dc.subject | Cross-database experimen | ca_CA |
dc.title | Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1016/j.imavis.2013.11.001 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleListID=-926196501&_sort=r&_st=13&view=c&md5=fd2af3fa857725aab127d6b16f4d90bd&searchtype=a | ca_CA |
dc.edition | Preprint | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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