An experimental comparison of gender classification methods

Successful face analysis requires robust methods. It has been hard to compare the methods due to different experimental setups. We carried out a comparison study for the state-of-the-art gender classification methods to find out their actual reliability. The main contributions are comprehensive and comparable classification results for the gender classification methods combined with automatic real-time face detection and, in addition, with manual face normalization. We also experimented by combining gender classifier outputs arithmetically. This lead to increased classification accuracies. Furthermore, we contribute guidelines to carry out classification experiments, knowledge on the strengths and weaknesses of the gender classification methods, and two new variants of the known methods.