FaceID-GAN: Learning a Symmetry Three-Player GAN
for Identity-Preserving Face Synthesis
Abstract
Face synthesis has achieved advanced development by
using generative adversarial networks (GANs). Existing
methods typically formulate GAN as a two-player game,
where a discriminator distinguishes face images from the
real and synthesized domains, while a generator reduces
its discriminativeness by synthesizing a face of photorealistic quality. Their competition converges when the
discriminator is unable to differentiate these two domains.
Unlike two-player GANs, this work generates identitypreserving faces by proposing FaceID-GAN, which treats a
classifier of face identity as the third player, competing with
the generator by distinguishing the identities of the real and
synthesized faces (see Fig.1). A stationary point is reached
when the generator produces faces that have high quality
as well as preserve identity. Instead of simply modeling the
identity classifier as an additional discriminator, FaceIDGAN is formulated by satisfying information symmetry,
which ensures that the real and synthesized images are
projected into the same feature space. In other words, the
identity classifier is used to extract identity features from
both input (real) and output (synthesized) face images of
the generator, substantially alleviating training difficulty
of GAN. Extensive experiments show that FaceID-GAN is
able to generate faces of arbitrary viewpoint while preserve
identity, outperforming recent advanced approaches