Abstract
In recent years, the deep learning based face inpainting methods have achieved some promising results, mainly related to the generative adversarial networks (GAN). Based on a large number of training samples, GAN generates a false true from a known training sample, and cannot use the trained parameters to generate image other than training samples. In addition, most of the previous works did not take into account high-level facial structure information, such as the co-existence of facial action units. In order to better to exploit facial structural knowledge, this paper proposes a method that combines prior knowledge based on high-level dynamic structural information of facial action units and GAN to complete face images. We primarily validate the effectiveness of our approach in facial expression restoration during face inpainting on the two datasets of BP4D and DISFA.
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