In this paper we investigate the feasibility of using synthetic data to
augment face datasets. In particular, we propose a novel generative adversarial
network (GAN) that can disentangle identity-related attributes from
non-identity-related attributes. This is done by training an embedding network
that maps discrete identity labels to an identity latent space that follows a
simple prior distribution, and training a GAN conditioned on samples from that
distribution. Our proposed GAN allows us to augment face datasets by generating
both synthetic images of subjects in the training set and synthetic images of
new subjects not in the training set. By using recent advances in GAN training,
we show that the synthetic images generated by our model are photo-realistic,
and that training with augmented datasets can indeed increase the accuracy of
face recognition models as compared with models trained with real images alone.

本文研究使用合成数据来增强面部数据集的可行性。我们提出了一种新颖的生成对抗网络（GAN），可以将相关身份属性与非相关身份属性区分开来。我们的 GAN 能够生成数据增强的合成图像，并且该模型可以提高面部识别模型的准确率。