Symmetry-Aware Face Completion with Generative Adversarial Networks

Abstract

Face completion is a challenging task in computer vision. Unlike general images, face images usually have strong semantic correlation and symmetry. Without taking these characteristics into account, existing face completion techniques usually fail to produce a photo-realistic result, especially for the missing key components (e.g., eyes and mouths). In this paper, we propose a symmetry-aware face completion method based on facial structural features using a deep generative model. The model is trained with a combination of a reconstruction loss, a structure loss, two adversarial losses and a symmetry loss, which ensures pixel faithfulness, local-global contents integrity and symmetrical consistency. We conduct a dedicated symmetry detection technique for facial components and show that the symmetrical attention module significantly improves face completion results. Experiments show that our method is capable of synthesizing semantically valid and visually plausible contents for the missing facial key parts from random mask. In addition, our model outperforms other methods for detail completion of facial components.