Photo-realistic face age progression/regression using a single generative adversarial network

Abstract

Face age progression/regression is enjoying renewed interest due to the remarkable improvements in image synthesis achieved by the deep generative models (e.g. the Generative Adversarial Networks (GANs)) and its tremendous impact on a wide-range of practical applications like finding back missing individuals with photos of childhood, entertainment, etc. Most existing approaches are focusing on face age progression and have proven to be successful and effective in learning the transformation between age groups with the aid of paired samples, i.e., face images of the same person at different ages. Although some signs of aging are synthesized by these approaches, they heavily rely on the availability of paired samples which are difficult and costly to collect. Inspired by the significant success achieved by using GANs in unsupervised image transduction, in this paper, we formulate this task as an unsupervised multi-domain image-to-image translation problem, and devise a novel generative framework using only a single generative adversarial network, dubbed FaceGAN which is capable of synthesizing photo-realistic face images with aging effects without paired samples and achieves face age progression and regression in a holistic framework. Experimental results show the superiority of our proposed method in terms of visual fidelity. We further empirically demonstrate the broad application capability of our approach on a facial attribute transfer and a facial expression synthesis tasks.