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In this paper, we propose a novel fusion-based gender classification method for 3D frontal neutral expression facial shape. Face landmarks, extracted from 3D face shape based on profiles and curvature, are separated as four regions. Experimental investigation to evaluate the significance of different facial regions in the task of gender classification is performed. The classification is performed by using Support Vector Machines (SVMs) based on the feature of regions. Classification results show that the upper region of face contains the highest amount of gender information. Matcher weighting fusion method is also applied to fusion the classification result of four regions. Experimental results demonstrate that fusing multiple facial features can achieve highest correct classification rate to 94.3%.