  Generative Adversarial Networks (GANs) are now capable of producing synthetic
face images of exceptionally high visual quality. In parallel to the
development of GANs themselves, efforts have been made to develop metrics to
objectively assess the characteristics of the synthetic images, mainly focusing
on visual quality and the variety of images. Little work has been done,
however, to assess overfitting of GANs and their ability to generate new
identities. In this paper we apply a state of the art biometric network to
various datasets of synthetic images and perform a thorough assessment of their
identity-related characteristics. We conclude that GANs can indeed be used to
generate new, imagined identities meaning that applications such as
anonymisation of image sets and augmentation of training datasets with
distractor images are viable applications. We also assess the ability of GANs
to disentangle identity from other image characteristics and propose a novel
GAN triplet loss that we show to improve this disentanglement.
