Date: January 2024.
Source: Pattern Recognition, Volume 145, 109970. ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2023.109970.
Highlights:
• A pretrained model on augmented data and fine-tuned by using AI and 3D contour features for assessing facial attractiveness is proposed.
• The proposed model overcomes the subjective inconsistency and unreliability common to all traditional rating methods.
• This model provides an accurate 3D information of full facial that is unavailable in previous studies using either 2D or 3D measurement.
• 3D facial images and deep transfer learning have been firstly combined for evaluating the facial attractiveness in patients undergoing OGS.
• The developed web browser–based user interface contributes to effective doctor–patient communication and decision-making.
Abstract: 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.
Article: A quantitative method for the assessment of facial attractiveness based on transfer learning with fine-grained image classification.
Authors: Lun-Jou Lo, Chao-Tung Yang, Wen-Chung Chiang, Hsiu-Hsia Lin. Department of Plastic and Reconstructive Surgery and Craniofacial Research Center, Chang Gung Memorial Hospital; Chang Gung University, Taoyuan, Taiwan.