Gender Classification with Bayesian Kernel Methods
Hyun-Chul Kim, Daijin Kim, Zoubin Ghahramani, Sung Yang Bang · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
We consider 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. Recently, SVMs which are popular kernel classifiers have been applied to gender classification and have shown excellent performance. We propose to use one of Bayesian kernel methods which is Gaussian Process Classifiers (GPCs) for gender classification. The main advantage of Bayesian kernel methods such as GPCs over SVMs is that they determine the hyperparameters of the kernel based on Bayesian model selection criterion. Our results show that GPCs outperformed SVMs with cross validation.