Identity preserving face completion with generative adversarial networks

Abstract

As a special application of image completion technology,face image completion has an irreplaceable role in the occlusion of face recognition,portrait restoration and other issues.The existing face completion algorithm only aims at complementing the authenticity of the image without considering its identity consistency after completion.A face complement algorithm based on improved generative confrontation network was designed.By introducing SN-GAN algorithm,the stability of model training was improved.At the same time,the identity recognition constraint was added to the generated image using the face recognition model.Experiments have shown that the proposed method can effectively maintain the identity of the complementary image when generating high-authenticity images.