The cluster assessment of facial attractiveness using fuzzy neural network classifier based on 3D Moiré features. WC Chiang, HH Lin, CS Huang, LJ Lo, SY Wan.

Date: March 2014.
Source: Pattern Recognition. Volume 47, Issue 3, pp 1249–1260.
Abstract: Facial attractiveness has long been argued upon varied emphases by philosophers, artists, psychologists and biologists. A number of studies empirically investigated how facial attractiveness was influenced by 2D facial characteristics, such as symmetry, averageness and golden ratio. However, few implementations of facial beauty assessment were based on 3D facial features. The purpose of this paper is to propose a novel cluster assessment system for facial attractiveness that is characterized by the incorporation of 3D geometric Moiré features with an adjusted fuzzy neural network (FNN). We first extract 3D facial features from images acquired by a 3dMD scanner. Seven Moiré features are employed to represent a 3D facial image. The FNN classifier, taking the Moiré features as the parameters, is then trained and validated against independently conducted attractiveness ratings. A number of diverse referees were invited and offered their attractiveness ratings over a five-item Likert scale for 100 female facial images. The proposed assessment presents a high accuracy rate of 90%, and the area under curve (AUC) computed from the receiver operating characteristic (ROC) curve is 0.95. The results show that the perceptions of facial attractiveness are essentially consensus among raters, and can be mathematically modeled through supervised learning techniques. The high accuracy achieved proves that the proposed FNN classifier can serve as a general, automated and human-like judgment tool for objective classification of female facial attractiveness, and thus has potential applications to the entertainment industry, cosmetic industry, virtual media, and plastic surgery.
Article: The cluster assessment of facial attractiveness using fuzzy neural network classifier based on 3D Moiré features.
Authors: Wen-Chung Chiang, Hsiu-Hsia Lin, Chiung-Shing Huang, Lun-Jou Lo, Shu-Yen Wan.