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.

研究使用生成的合成数据进行面部识别的训练。通过在 GAN 的产生器中引入 3D 可塑模型，实现了从实际图像中学习非线性纹理模型，从而生成新的合成身份，同时操作姿势、照明和表情，而不会影响身份。最终使用生成的数据增加现有面部识别网络的训练，性能得到改善。