Face Age Transformation with Progressive Residual Adversarial Autoencoder

Face age transformation is an important issue in many applications. While unidirectional and short-span face ageing has achieved remarkable progress, it remains a challenging problem to generate both younger-look and older-look face images over long age span. In this paper, we present a progressive residual adversarial autoencoder (PRAA) model for bidirectional and long-span face age transformation. Given an input face image, our model aims to synthesize face images of its younger looks (face rejuvenation) and older looks (face ageing). The PRAA contains adversarial generators and discriminators where age information is encoded as latent features. It adopts a residual-encoding strategy by which the original face images and the residual face images are jointly encoded. We adopt a progressive multi-scale method to train the network, by which our model can capture both the global structure and the local detail changes in face age transformation. We test our model on challenging data and the experimental results prove the strength of our method.