Facial Beauty Prediction Using a Generative Adversarial Network for Dataset Augmentation

Facial beauty prediction (FBP) is a significant research direction in the field of computer vision; however, the performance of models developed for this task is often constrained due to the scarcity of high-quality annotated data. Generative adversarial networks (GANs) are efficient image generation networks that are capable of rapidly generating facial images. This study proposes an FBP method—named FBP-GAN—which aims to address this shortage of data by generating high-quality synthetic facial images. First, we construct a facial image generation network based on StyleGAN2-ADA to generate diverse and realistic facial images. Second, we combine transfer learning and data augmentation techniques to utilize the synthesized images for training set augmentation while optimizing the category distribution to enhance the generalization ability and prediction accuracy of the classification network. The experimental results demonstrate that, when using MobileViT or ResNeXt as the classification network, our proposed approach achieves prediction accuracies of 76.38% and 77.94% on the SCUT-FBP5500 dataset, respectively, representing improvements of 0.55% and 1.65% over the baseline models’ 75.83% and 76.29%. The proposed approach effectively improves the accuracy of FBP under data-scarce scenarios and opens new avenues for the application of GANs in computer vision tasks.