Abstract

Face aging, also known as age progression, is attracting more and more research interests. It has plenty of applications in various domains including cross-age face recognition, nding lost children, and entertainments. In recent years, face aging has witnessed various breakthroughs and a number of face aging models have been proposed [3]. Face aging, however, is still a very challenging task in practice for various reasons. First, faces may have many dierent expressions and lighting conditions, which pose great challenges to modeling the aging patterns. Besides, the training data are usually very limited and the face images for the same person only cover a narrow range of ages.

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