Facial Expression Restoration Based on Improved Graph Convolutional Networks
Abstract
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.
Cite
@article{arxiv.1910.10344,
title = {Facial Expression Restoration Based on Improved Graph Convolutional Networks},
author = {Zhilei Liu and Le Li and Yunpeng Wu and Cuicui Zhang},
journal= {arXiv preprint arXiv:1910.10344},
year = {2020}
}
Comments
Accepted by MMM2020