Facial Aesthetic Enhancement Network for Asian Faces Based on Differential Facial Aesthetic Activations

In this paper, we addressed facial aesthetic enhancement (FAE). Although existing methods have made great progress, the beautified images generated by them are highly prone to poor beautification, which limits their application to real-world scenes. To tackle this problem, we proposed a new method called the facial aesthetic enhancement network for Asian faces based on differential facial aesthetic activations (Diff-FANet), which comprises three important modules: aesthetic average difference perception block (ADP), aesthetic difference evaluation block (ADE), and aesthetic fusion optimization block (AFO). ADP learns the transformation of the latent code of an image before and after beautification. The ADE learns the features of an enhanced image, which guides image fusion. The AFO was used to eliminate ghosting. To evaluate the effectiveness of Diff-FANet, we utilized the wedding dataset for training and the SCUT-FBP5500 and Asian face datasets for testing. The results of experiments revealed that Diff-FANet achieved excellent results.