Assessing The Effect Of Crossing Databases On Global And Local Approaches For Face Gender Classification

This paper presents a comprehensive statistical study of the suitability of global and local approaches for face gender classification from frontal non-occluded faces. A realistic scenario is simulated with cross-database experiments where acquisition and demographic conditions considerably vary between training and test images. The performances of three classifiers (1-NN, PCA+ LDA and SVM) using two types of features (grey levels and PCA) are compared for the two approaches. Supported by three statistical tests, the main conclusion extracted from the experiments is that if training and test faces are acquired under different conditions from diverse populations, no significant differences exist between global and local solutions. However, global methods outperform local models when training and test sets contain only images of the same database.