Face Mask Removal Based on Generative Adversarial Network and Texture Network
Xiaolin Li, Changcheng Shao, Yifan Zhou, Lei Huang · 2021
In recent years, the problem of image complementation has achieved better complementation effect under the condition of applying deep learning technology, and even the human eye is difficult to distinguish. Therefore, the technology has also become a hot research topic on image complementation. At the same time, how to modify the generative adversarial network to better adapt to the problem of image complementation to construct a more efficient generation model has been paid more and more attention. Face complementation, as a branch of the problem of picture complementation, is a common face image editing technique, which can also be used to edit face properties. The resulting face image can be as accurate as the original face image, or it can be consistent with the content of the unobstructed face image so that the completed image appears to have a real visual feel. Typically, the Generative Adversarial Network can generate missing parts of the face, but the results generated are often incongruous with the entire face, the texture is incoherent, the context is not integrated, and there are obvious signs of repair at the connection between the missing area and the non-missing area. This paper proposes the mode of combining Generative Adversarial Network and Texture Network. Generative Adversarial Network by training a large number of samples, generating an image of the face mask area, and then Texture Network smoothing the image, adding textures, making the resulting face more realistic. Experimental results from the CelebA dataset show that our model can handle a large number of missing pixels and produce realistic facial completion results.