  Face aging simulation has received rising investigations nowadays, whereas it
still remains a challenge to generate convincing and natural age-progressed
face images. In this paper, we present a novel approach to such an issue by
using hidden factor analysis joint sparse representation. In contrast to the
majority of tasks in the literature that handle the facial texture integrally,
the proposed aging approach separately models the person-specific facial
properties that tend to be stable in a relatively long period and the
age-specific clues that change gradually over time. It then merely transforms
the age component to a target age group via sparse reconstruction, yielding
aging effects, which is finally combined with the identity component to achieve
the aged face. Experiments are carried out on three aging databases, and the
results achieved clearly demonstrate the effectiveness and robustness of the
proposed method in rendering a face with aging effects. Additionally, a series
of evaluations prove its validity with respect to identity preservation and
aging effect generation.
