Gender classification using M-estimator based radial basis function neural network

A gender classification method using an M-estimator based radial basis function (RBF) neural network is proposed in this paper. In the proposed method, three types of effective features, including facial texture features, hair geometry features, and moustache features are extracted from a face image. Then, an improved RBF neural network based on M-estimator is proposed to classify the gender according to the extracted features. The improved RBF network uses an M-estimator to replace the traditional least-mean square (LMS) criterion to deal with the outliers in the data set. The FERET database is used to evaluate our method in the experiment. In the FERET data set, 600 images are chosen in which 300 of them are used as training data and the rest are regarded as test data. The experimental results show that the proposed method can produce a good performance.