Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes
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Other documents of the author: Andreu Cabedo, Yasmina; García-Sevilla, Pedro; Mollineda, Ramón A.
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Title
Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposesDate
2014-01xmlui.dri2xhtml.METS-1.0.item-edition
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ElsevierISSN
0262-8856Bibliographic 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.Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleLis ...Version
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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 ... [+]
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. [-]
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Image and Vision Computing, 2014, vol. 32, no 1Rights
© 2013 Elsevier B.V. All rights reserved.
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