A gender classification approach based on 3D depth-radial curves and fuzzy similarity based classification

We propose in this paper, a gender recognition solution under the presence of occlusion and using the very restrict samples in the learning base. The developed approach is based on the extraction of pertinent 3D depth-radial curves that cover the nose region and combined dimensionality reduction using sparse random projection method; furthermore we propose an extension of similarity based classification approach to handle recognition task. Experimental results approve the effectiveness of our approach and show that the proposed method is also effective in the presence of variations such as facial expressions and rotation.