On the importance of multi-dimensional information in gender estimation from face images

Abstract

Estimating human face demography from images is a problem that has recently been extensively studied because of its relevant applications. We review state-of-the-art approaches to gender classification and confirm that their performance drops significantly when classifying young or elderly faces. We hypothesize that this is caused by the existence of dependencies among the demographic variables that were not considered in traditional gender classifiers. In the paper we confirm experimentally the existence of such dependencies between age and gender variables. We also prove that the performance of gender classifiers can be improved by considering the dependencies with age in a multi-dimensional approach. The performance improvement is most prominent for young and elderly faces. © 2011 Springer-Verlag.