Gender classification based on lossy data coding

This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. In this paper, we present a simple new criterion for classification, based on principles from lossy data compression. The criterion assigns a test sample to the class that uses the minimum number of additional bits to code the test sample, subject to an allowable distortion. This formulation induces several good effects on the resulting classifier. First, minimizing the lossy coding length induces a regularization effect which stabilizes the (implicit) density estimate in a small sample setting. Second, compression provides a uniform means of handling classes of varying dimension. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets.