Facial Beauty Prediction Using Global Context Vision Transformer

Facial beauty prediction (FBP) has garnered significant attention in recent years due to its wide range of applications in cosmetics, entertainment, and healthcare. Traditional approaches rely heavily on handcrafted features or deep convolutional neural networks (CNNs), which often miss the global contextual information crucial for accurate prediction. This paper presents a novel method for facial beauty prediction using a Global Context Vision Transformer (GCViT). By leveraging the Vision Transformer's ability to model long-range dependencies and global relationships between facial features, our approach enhances the prediction accuracy beyond the limitations of CNN-based models. The proposed model integrates CNN-based feature extraction and Transformer-based attention mechanisms to effectively model intricate facial details and global context. A hierarchical architecture is used to progressively refine features across multiple resolutions, enabling the model to focus on fine-grained facial attributes while maintaining global consistency. We evaluate the performance of our model on the SCUT-FBP5500 dataset, achieving state-of-the-art results 0.9456 with significantly higher Pearson Correlation compared to existing methods. The experimental results demonstrate that the GC ViT outperforms traditional CNN-based approaches, offering a more effective solution for facial beauty prediction tasks.