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

2022: Gwangbin Bae, M. D. L. Gorce, T. Baltrušaitis, Charlie Hewitt, Dong Chen, Julien P. C. Valentin, R. Cipolla, JingJing Shen

Ranked #1 on Face Recognition on AgeDB

https://arxiv.org/pdf/2210.02579v1.pdf