  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.
