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Proceedings Paper

Gender classification from neutral and expressive faces
Author(s): Yasmina Andreu; Pedro García-Sevilla; Ramón A. Mollineda
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Paper Abstract

This paper presents a statistical study of local vs. global approaches for classifying gender from neutral and expressive faces. A cross-dataset evaluation is provided by using different training and test face databases, as well as several well-known classifiers (1-NN, PCA+LDA and SVM) and widely used features for facial description. Three statistical tests have proved that local approaches are more suitable than global ones for solving gender classification problems over expressive faces when training with non-expressive faces. However, if a large set of expressive faces is available for training, global solutions outperform local ones.

Paper Details

Date Published: 24 December 2013
PDF: 6 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 906723 (24 December 2013); doi: 10.1117/12.2051041
Show Author Affiliations
Yasmina Andreu, Univ. Jaume I (Spain)
Pedro García-Sevilla, Univ. Jaume I (Spain)
Ramón A. Mollineda, Univ. Jaume I (Spain)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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