  Face synthesis, including face aging, in particular, has been one of the
major topics that witnessed a substantial improvement in image fidelity by
using generative adversarial networks (GANs). Most existing face aging
approaches divide the dataset into several age groups and leverage group-based
training strategies, which lacks the ability to provide fine-controlled
continuous aging synthesis in nature. In this work, we propose a unified
network structure that embeds a linear age estimator into a GAN-based model,
where the embedded age estimator is trained jointly with the encoder and
decoder to estimate the age of a face image and provide a personalized target
age embedding for age progression/regression. The personalized target age
embedding is synthesized by incorporating both personalized residual age
embedding of the current age and exemplar-face aging basis of the target age,
where all preceding aging bases are derived from the learned weights of the
linear age estimator. This formulation brings the unified perspective of
estimating the age and generating personalized aged face, where self-estimated
age embeddings can be learned for every single age. The qualitative and
quantitative evaluations on different datasets further demonstrate the
significant improvement in the continuous face aging aspect over the
state-of-the-art.
