A quantitative method for the assessment of facial attractiveness based on transfer learning with fine-grained image classification

In this paper, we investigate a new approach based on a combination of three-dimensional (3D) facial images and deep transfer learning (TL) with fine-grained image classification (FGIC) for quantitative evaluation of facial attractiveness. The 3D facial surface images of patients with and without filtering and the publicly available SCUT-FBP5500 dataset was used for transfer training and model pre-training, respectively. Experimental results show that a bilinear CNN model with a Gaussian filter freezing 80 % of the weights exhibit the strongest performance and lowest average error as a deep learning prediction model; the model was subsequently adopted for automatic assessment of facial attractiveness in clinical application. This is the first TL model with FGIC using 3D facial images for automatic quantitative evaluation of facial attractiveness in patients undergoing Orthognathic surgery (OGS). The developed web browser-based user interface enables effective and rapid assessment, thus contributing to effective patient-clinician communication and decision-making.