Facial Beauty Prediction with Vision Transformer

Abstract

In addition to its use in the realm of plastic surgery and aesthetics, Facial Beauty Prediction technology also has applications in other areas, such as advertising and social media, where it can be used to optimize marketing strategies and help individuals enhance their online presence. There are applications in other areas as well, such as advertising and social media. This study introduces an effective approach to evaluate human face beauty using a transformer-based architecture. While Convolutional Neural Network (CNN) is a conventional method for this task, our experimental results demonstrate that our Vision Transformer (ViT) based model outperforms the other two effective baselines, VGGNet and ResNet50, in evaluating human face beauty on the widely-used benchmark dataset SCUT-FPB 5500. Our ViT-based model demonstrates superior performance in Mean Absolute Error (MAE) and Mean Squared Error (MSE) compared to VGG16 and ResNet-50, despite employing a simple data pipeline without any data augmentation. Our study suggests that transformer-based architectures offer a more effective means of evaluating human beauty and open new avenues for further research in this field.