Gender Recognition using Adaboosted Feature

In this paper, a novel approach for gender recognition combining the ellipse face images, Gabor filters, Adaboost learning and SVM classifier is proposed. Face representation based on Harr-like feature, Gabor feature or ICA is an effective method to extract facial appearance information. So we compare these three kinds of features selected by Adaboost method using FERET database. In the first experiment, several different preprocessing methods (face detector, warp face images and ellipse face images) have been compared, meanwhile comparing different feature extraction methods (Gabor wavelets, Haar-like wavelets, PCA, ICA).The experimental results show that our proposed approach (combination of ellipse face images, Gabor wavelets and Ada+SVM classifier) achieves better performance. The second experiment is tested on PCA and ICA feature extraction method with different explanation. It is shown that ICA is much steadier than PCA method when the explanation changed.