  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 subject-specific 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.
