  The vanilla Generative Adversarial Networks (GAN) are commonly used to
generate realistic images depicting aged and rejuvenated faces. However, the
performance of such vanilla GANs in the age-oriented face synthesis task is
often compromised by the mode collapse issue, which may result in the
generation of faces with minimal variations and a poor synthesis accuracy. In
addition, recent age-oriented face synthesis methods use the L1 or L2
constraint to preserve the identity information on synthesized faces, which
implicitly limits the identity permanence capabilities when these constraints
are associated with a trivial weighting factor. In this paper, we propose a
method for the age-oriented face synthesis task that achieves a high synthesis
accuracy with strong identity permanence capabilities. Specifically, to achieve
a high synthesis accuracy, our method tackles the mode collapse issue with a
novel Conditional Discriminator Pool (CDP), which consists of multiple
discriminators, each targeting one particular age category. To achieve strong
identity permanence capabilities, our method uses a novel Adversarial Triplet
loss. This loss, which is based on the Triplet loss, adds a ranking operation
to further pull the positive embedding towards the anchor embedding resulting
in significantly reduced intra-class variances in the feature space. Through
extensive experiments, we show that our proposed method outperforms
state-of-the-art methods in terms of synthesis accuracy and identity permanence
capabilities, qualitatively and quantitatively.
