[hal-01074090] Combining Face Averageness and Symmetry for 3D-based Gender Classification

Although human face averageness and symmetry are valuable clues in social perception (such as attractiveness, masculinity/femininity, healthy/sick, etc.), in the literature of facial attribute recognition, little consideration has been given to them. In this work, we propose to study the morphological differences between male and female faces by analyzing the averageness and symmetry of their 3D shapes. In particular, we address the following questions: (i) is there any relationship between gender and face averageness/symmetry? and (ii) if this relationship exists, which specific areas on the face are involved? To this end, we propose first to capture densely both the face shape averageness (AVE) and symmetry (SYM) using our Dense Scalar Field (DSF), which denotes the shooting directions of geodesics between facial shapes. Then, we explore such representations by using classical machine learning techniques, the Feature Selection (FS) methods and Random Forest (RF) classification algorithm. Experiments conducted on the FRGCv2 dataset show a significant relationship exists between gender and facial averageness/symmetry when achieving a classification rate of 93.7% on the 466 earliest scans of subjects (mainly neutral) and 92.4% on the whole FRGCv2 dataset (including facial expressions)



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