CAAE++: Improved CAAE for Age Progression/Regression

Abstract

Face age progression/regression has garnered substantial active research interest due to its tremendous impact on a wide-range of practical applications like searching for missing individuals with photos of childhood, entertainment, and so on. Most existing face aging models 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. Considering the expensive cost of collecting paired datasets, Conditional Adversarial Autoencoder (CAAE) is designed for face aging task without paired samples and first achieves face age progression and regression in a holistic framework. However, only rough wrinkles are generated because of the insufficient discriminative and generative ability. To tackle this problem, in this paper, we develop a novel generative model based on CAAE, dubbed CAAE++, which defeats the previous CAAE mainly for two enhancements: 1) an auxiliary classifier is added on top of the discriminator, which allows a single discriminator not only distinguishes real images from synthetics but also classifies them into the target age group; and 2) a pre-trained deep face recognition model and a pre-trained age estimation model are exploited to preserve identity and age similarity, respectively. We train CAAE++ on UTKFace dataset and test on FGNET dataset. Experimental results demonstrate the efficacy of our proposed method in terms of fidelity.

Keywords

• Age progression/regression
• generative adversarial networks
• conditional adversarial autoencoder
• image generation
• generators
• face recognition