DigiFace-1M: 1 Million Digital Face Images for Face Recognition

• URL: http://arxiv.org/abs/2210.02579v1
• Date: Wed, 5 Oct 2022 22:02:48 GMT
• Title: DigiFace-1M: 1 Million Digital Face Images for Face Recognition
• Authors: Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen
• Abstract summary: State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild dataset. We introduce a large-scale synthetic dataset for face recognition, obtained by rendering digital faces using a computer graphics pipeline.
• Score: 25.31469201712699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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