  Facial recognition using deep convolutional neural networks relies on the
availability of large datasets of face images. Many examples of identities are
needed, and for each identity, a large variety of images are needed in order
for the network to learn robustness to intra-class variation. In practice, such
datasets are difficult to obtain, particularly those containing adequate
variation of pose. Generative Adversarial Networks (GANs) provide a potential
solution to this problem due to their ability to generate realistic, synthetic
images. However, recent studies have shown that current methods of
disentangling pose from identity are inadequate. In this work we incorporate a
3D morphable model into the generator of a GAN in order to learn a nonlinear
texture model from in-the-wild images. This allows generation of new, synthetic
identities, and manipulation of pose, illumination and expression without
compromising the identity. Our synthesised data is used to augment training of
facial recognition networks with performance evaluated on the challenging CFP
and CPLFW datasets.
