Gender Classification with Support Vector Machines Based on Non-tensor Pre-wavelets
Ying Li, Yù Zhang, Zhao Shishun · 2010
In this paper, a novel method for gender classifications with support vector machines based on our constructed bivariate compactly supported non-tensor product pre-wavelets is proposed. Utilizing the non-tensor product pre-wavelets to extract the more excellent gender classification features, then these features are fed into support vector machines to automatically perform gender classification. The combination of the non-tensor product pre-wavelets and SVMs for gender classification is demonstrated to be efficient by concrete numerical experiments.