Gender and 3D facial symmetry: What's the relationship?

Although it is valuable information that human faces are approximately symmetric, in the literature of facial attributes recognition, little consideration has been given to the relationship between gender, age, ethnicity, etc. and facial asymmetry. In this paper we present a new approach based on bilateral facial asymmetry for gender classification. For that purpose, we propose to first capture the facial asymmetry by using Deformation Scalar Field (DSF) applied on each 3D face, then train such representations (DSFs) with several classifiers, including Random Forest, Adaboost and SVM after PCA-based feature space transformation. Experiments conducted on FRGCv2 dataset showed that a significant relationship exists between gender and facial symmetry when achieving a 90.99% correct classification rate for the 466 earliest scans of subjects (mainly neutral) and 88.12% on the whole FRGCv2 dataset (including facial expressions).