  Face aging, which aims at aesthetically rendering a given face to predict its
future appearance, has received significant research attention in recent years.
Although great progress has been achieved with the success of Generative
Adversarial Networks (GANs) in synthesizing realistic images, most existing
GAN-based face aging methods have two main problems: 1) unnatural changes of
high-level semantic information (e.g. facial attributes) due to the
insufficient utilization of prior knowledge of input faces, and 2) distortions
of low-level image content including ghosting artifacts and modifications in
age-irrelevant regions. In this paper, we introduce A3GAN, an Attribute-Aware
Attentive face aging model to address the above issues. Facial attribute
vectors are regarded as the conditional information and embedded into both the
generator and discriminator, encouraging synthesized faces to be faithful to
attributes of corresponding inputs. To improve the visual fidelity of
generation results, we leverage the attention mechanism to restrict
modifications to age-related areas and preserve image details. Moreover, the
wavelet packet transform is employed to capture textural features at multiple
scales in the frequency space. Extensive experimental results demonstrate the
effectiveness of our model in synthesizing photorealistic aged face images and
achieving state-of-the-art performance on popular face aging datasets.
