Combining face averageness and symmetry for 3D-based gender classification

• Published in: Pattern recognition Vol. 48; no. 3; pp. 746 - 758
• Main Authors: Xia, Baiqiang, Ben Amor, Boulbaba, Drira, Hassen, Daoudi, Mohamed, Ballihi, Lahoucine
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2015; Elsevier
• Subjects: 3D Face; Classification; Computer Science; Computer Vision and Pattern Recognition; Dense scalar field; Face averageness; Face recognition; Face symmetry; Facial; Feature selection; Gender classification; Pattern recognition; Perception; Random Forest; Scalars; Symmetry; Three dimensional; Dense scalar field; Feature selection; 3D Face; Face averageness; Face symmetry; Gender classification; Random Forest
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2014.09.021

Although human face averageness and symmetry are valuable clues in social perception (such as attractiveness, masculinity/femininity, and healthy/ sick), 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 that 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). •New Dense Scalar Fields grounding on Riemannian Geometry for 3D facial shape analysis.•New averageness and symmetry descriptors for gender classification.•Combining averageness and symmetry for better gender classification.•Competitive classification results with state-of-the-art.