  State-of-the-art face recognition models show impressive accuracy, achieving
over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained
on large-scale datasets that contain millions of real human face images
collected from the internet. Web-crawled face images are severely biased (in
terms of race, lighting, make-up, etc) and often contain label noise. More
importantly, the face images are collected without explicit consent, raising
ethical concerns. To avoid such problems, we introduce a large-scale synthetic
dataset for face recognition, obtained by rendering digital faces using a
computer graphics pipeline. We first demonstrate that aggressive data
augmentation can significantly reduce the synthetic-to-real domain gap. Having
full control over the rendering pipeline, we also study how each attribute
(e.g., variation in facial pose, accessories and textures) affects the
accuracy. Compared to SynFace, a recent method trained on GAN-generated
synthetic faces, we reduce the error rate on LFW by 52.5% (accuracy from 91.93%
to 96.17%). By fine-tuning the network on a smaller number of real face images
that could reasonably be obtained with consent, we achieve accuracy that is
comparable to the methods trained on millions of real face images.
