Gender Classification Based on 3D Face Geometry Features Using SVM

In this work we have used non-linear Support Vector Machines (SVMs) for gender classification. The SVMis applied to triangular meshes representing human faces. In this work we rely on handful of 3-dimensional facial features which are extracted from the corresponding geometry meshes. The experimental results show that in our method the error rate is 17.44% on average. It is thought that the approach used to determine gender prior to face recognition would make an automatic face recognition system more efficient.