  Face aging is to render a given face to predict its future appearance, which
plays an important role in the information forensics and security field as the
appearance of the face typically varies with age. Although impressive results
have been achieved with conditional generative adversarial networks (cGANs),
the existing cGANs-based methods typically use a single network to learn
various aging effects between any two different age groups. However, they
cannot simultaneously meet three essential requirements of face aging --
including image quality, aging accuracy, and identity preservation -- and
usually generate aged faces with strong ghost artifacts when the age gap
becomes large. Inspired by the fact that faces gradually age over time, this
paper proposes a novel progressive face aging framework based on generative
adversarial network (PFA-GAN) to mitigate these issues. Unlike the existing
cGANs-based methods, the proposed framework contains several sub-networks to
mimic the face aging process from young to old, each of which only learns some
specific aging effects between two adjacent age groups. The proposed framework
can be trained in an end-to-end manner to eliminate accumulative artifacts and
blurriness. Moreover, this paper introduces an age estimation loss to take into
account the age distribution for an improved aging accuracy, and proposes to
use the Pearson correlation coefficient as an evaluation metric measuring the
aging smoothness for face aging methods. Extensively experimental results
demonstrate superior performance over existing (c)GANs-based methods, including
the state-of-the-art one, on two benchmarked datasets. The source code is
available at~\url{https://github.com/Hzzone/PFA-GAN}.
