Face aging on SiGan

Abstract

Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.