Abstract
The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not
well studied in the literature. In this paper, we present a
novel generative adversarial network based approach. It
separately models the constraints for the intrinsic subjectspecific characteristics and the age-specific facial changes
with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to
generate more lifelike facial details, high-level age-specific
features conveyed by the synthesized face are estimated by
a pyramidal adversarial discriminator at multiple scales,
which simulates the aging effects in a finer manner. The
proposed method is applicable to diverse face samples in
the presence of variations in pose, expression, makeup, etc.,
and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.