Both qualitative and quantitative experiments show that our method can generate appealing results in terms of image quality, personal identity, and age accuracy. Conditioned with age vectors, the primal cGAN in our symmetric network converts input faces to target ages, and the dual cGAN inverts the previous task, which feeds synthesized target faces back to the original input age scope for enhancing age consistency. We adapt a built-in attention mechanism to emphasize the most discriminative regions relevant to aging and minimize changes that affect personal identity and background. In this paper, we propose a novel Landmark-Guided cGAN (LGcGAN), which not only synthesizes texture changes related to aging, but also alters facial structures accordingly. However, generating a convincing aging appearance while preserving the person’s identity is still a challenging task.

Recently, conditional Generative Adversarial Networks (cGANs) have achieved visually impressive progress in this area. Face aging, which alters a person’s facial photo to the appearance at a different age, is a popular topic in multimedia applications.