Geometric based 3D facial gender classification

This paper addresses the issue of Gender Classification from 3D facial images. While most of previous work in the literature focuses on either 2D facial images, here, we study the use of 3D facial shape for automatic gender classification. After a preprocessing step to extract the facial masks from triangular meshes obtained using laser range scanners, we approximate the facial surfaces by collections of radial and iso-level curves. Once the curves are extracted, we aim at studying their shape using existant shape analysis framework which allows to compute similarities between a candidate face and Male and Female templates. We expect that the shape of certain curves are similar within Male/Female classes and different when moving from one class to another. For classification, we perform three Machine Learning algorithms (Adaboost, Neural Network, and SVM). Overall, Adaboost was superior in classification performance (84.98% as classification rate) on a subset of FRGCv2 dataset including the first (neutral and non-neutral) scans of different subjects. Our results indicate also that (i) the most relevant iso-level curves cover the central stripe of the face, and (ii) the most relevant radial curves are located on the upper part of the face.