Gender recognition with Gabor filters

The problem of gender identification was approached in this paper starting from images with faces. In order to extract features, Gabor filters were applied using various orientation angles in order to capture significant gender information. Different classifiers were tested (Support Vector Machines, k-NN, discriminant analysis, neural network) on images from the FERET and AR databases. We obtain very good identification results comparable with those obtained by state-of-the-art algorithms.