Gender classification from neutral and expressive faces
Yasmina Andreu, Pedro Garcı́a-Sevilla, Ramón A. Mollineda · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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.