Gender classification from neutral and expressive faces

Yasmina Andreu; Pedro García-Sevilla; Ramón A. Mollineda

doi:10.1117/12.2051041

24 December 2013 Gender classification from neutral and expressive faces

Yasmina Andreu, Pedro García-Sevilla, Ramón A. Mollineda

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 906723 (2013) https://doi.org/10.1117/12.2051041
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

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.

Citation Download Citation

Yasmina Andreu, Pedro García-Sevilla, and Ramón A. Mollineda "Gender classification from neutral and expressive faces", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 906723 (24 December 2013); https://doi.org/10.1117/12.2051041

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