  Image inpainting consists of filling holes or missing parts of an image.
Inpainting face images with symmetric characteristics is more challenging than
inpainting a natural scene. None of the powerful existing models can fill out
the missing parts of an image while considering the symmetry and homogeneity of
the picture. Moreover, the metrics that assess a repaired face image quality
cannot measure the preservation of symmetry between the rebuilt and existing
parts of a face. In this paper, we intend to solve the symmetry problem in the
face inpainting task by using multiple discriminators that check each face
organ's reality separately and a transformer-based network. We also propose
"symmetry concentration score" as a new metric for measuring the symmetry of a
repaired face image. The quantitative and qualitative results show the
superiority of our proposed method compared to some of the recently proposed
algorithms in terms of the reality, symmetry, and homogeneity of the inpainted
parts.
