Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
This is an incremental review paper summarizing existing methods for face aging in computer vision.
The paper reviews recent deep learning approaches, specifically deep generative models, for face aging, discussing their structures, learning algorithms, and synthesized results, along with the databases used.
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.