  Face Aging has raised considerable attentions and interest from the computer
vision community in recent years. Numerous approaches ranging from purely image
processing techniques to deep learning structures have been proposed in
literature. In this paper, we aim to give a review of recent developments of
modern deep learning based approaches, i.e. Deep Generative Models, for Face
Aging task. Their structures, formulation, learning algorithms as well as
synthesized results are also provided with systematic discussions. Moreover,
the aging databases used in most methods to learn the aging process are also
reviewed.
