# Facial Expression Restoration Based on Improved Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1910.10344 · HTML: https://mlsanity.com/paper/1910.10344/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Dynamic Attentive Graph Learning for Image Restoration](https://mlsanity.com/paper/2109.06620/) (2021)
- [Sharing Key Semantics in Transformer Makes Efficient Image Restoration](https://mlsanity.com/paper/2405.20008/) (2024)
- [Key-Graph Transformer for Image Restoration](https://mlsanity.com/paper/2402.02634/) (2024)
- [Dynamic Resolution Guidance for Facial Expression Recognition](https://mlsanity.com/paper/2404.06365/) (2024)
- [A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer](https://mlsanity.com/paper/2409.14198/) (2024)
- [Task-driven real-world super-resolution of document scans](https://mlsanity.com/paper/2506.06953/) (2025)

## Abstract (arXiv)

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.