Papers › DigiFace-1M: 1 Million Digital Face Images for Face Recognition
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen
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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Code
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Synthetic Face Recognition | AgeDB-30 | DigiFace-1M | Accuracy | 0.811 | #3 of 3 | Archive leaderboard | report |
| Synthetic Face Recognition | CALFW | DigiFace-1M | Accuracy | 0.8255 | #3 of 3 | Archive leaderboard | report |
| Synthetic Face Recognition | CFP-FP | DigiFace-1M | Accuracy | 0.8981 | #2 of 3 | Archive leaderboard | report |
| Synthetic Face Recognition | CPLFW | DigiFace-1M | Accuracy | 0.8223 | #2 of 3 | Archive leaderboard | report |
| Synthetic Face Recognition | LFW | DigiFace-1M | Accuracy | 0.9617 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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