Facial Expression Restoration Based on Improved Graph Convolutional Networks
This work addresses the problem of accurate facial expression analysis in real-world conditions for applications like human-computer interaction, though it appears incremental in its technical approach.
The paper tackles facial expression restoration from low-resolution or occluded images by proposing a method that integrates an improved graph convolutional network and region relation modeling block, achieving effective results on BP4D and DISFA benchmarks.
Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion. Considering the correlations among different facial local regions under different facial expressions, this paper proposes a novel facial expression restoration method based on generative adversarial network by integrating an improved graph convolutional network (IGCN) and region relation modeling block (RRMB). Unlike conventional graph convolutional networks taking vectors as input features, IGCN can use tensors of face patches as inputs. It is better to retain the structure information of face patches. The proposed RRMB is designed to address facial generative tasks including inpainting and super-resolution with facial action units detection, which aims to restore facial expression as the ground-truth. Extensive experiments conducted on BP4D and DISFA benchmarks demonstrate the effectiveness of our proposed method through quantitative and qualitative evaluations.