Cross-age face synthesis based on conditional adversarial autoencoder

Abstract

Face aging aims to render face images with desired age attribute. It has tremendous impact to a wide-range of applications, e.g., criminal investigation, entertainment. The rapid development of generative adversarial networks (GANs) has shown impressive results in face aging. Among them, the Conditional Adversarial Autoencoder (CAAE) proposed in 2017 has achieved good results in face aging. However, the generated faces still have the problems that the aging features are not obvious and the identity information are not well maintained. In addition, research have shown that the human aging process is affected by genes. Different races have different external characteristics of aging. However, the current research does not take the racial factor into account, ignores the racial differences in the aging process. It affects the accuracy of transformation. To solve the above problems, this paper proposes a cross-age face synthesis based on conditional adversarial autoencoder: First, a conditional adversarial autoencoder is used as the infrastructure to build a cross-age face synthesis model based on race constraints. Secondly, the discriminator is composed of a discriminant network and a classification network, and a category loss function is designed to generate a real face that matches the target age; Finally, the model uses a identity feature extractor and a discriminator of the multi-scale architecture. Through multi-level discrimination from pixel values to high-level semantic information, the loss of personal identity features is minimized. UTKFace and MegaAge-Asian datasets are used in the experiment. Three comparative experiments are designed for the above improvements. The results show that the racial constraints make the generated images effectively maintain the racial characteristics, such as skin color and texture; The classification function of the discriminator improves the aging effect; The design of the multi-scale discriminator enables the generated face to have more stable local structure and identity information. Through qualitative and quantitative analysis, it is shown that this method has higher aging accuracy and identity retention than the CAAE method.