Abstract: Facial gender classification is a challenging topic, and it's still not perfect until now. In this paper, we propose a series of methods of gender classification based on three-dimension faces. Automatic front-pose adjustment is needed through local region iterative closest point (ICP) registration firstly; then we do pitching rotating and extract multi-angle LBP features from depth thumbnail map in different viewing angles; at last, we use support vector machine (SVM) classifier to do training and prediction. This algorithm has been experimented on CASIA database, and for the neutral faces in this database, we can get a highest correct classification rate of 98.374%.