Single- and cross- database benchmarks for gender classification under unconstrained settings

Abstract

Gender classification is one of the most important tasks in automated face analysis, and has attracted the interest of researchers for years. Up to now, most gender classification approaches have been tested using single-database experiments, and on quite controlled datasets such as the FERET database, which are not representative of real world settings. However, a recent trend towards more realistic benchmarks has emerged within the face analysis community, leading to the appearance of databases and protocols such as the Labeled Faces in the Wild (LFW) database, and the so-called Gallagher's database, which comprises images collected from Flickr.