Summary of Variface: Fair and Diverse Synthetic Dataset Generation For Face Recognition, by Michael Yeung et al.
VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition
by Michael Yeung, Toya Teramoto, Songtao Wu, Tatsuo Fujiwara, Kenji Suzuki, Tamaki Kojima
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes VariFace, a two-stage diffusion-based pipeline to create synthetic face datasets that can train face recognition models fairly and accurately. Synthetic methods mitigate privacy and bias concerns when using web-scraped data, but existing datasets lack diversity and performance. VariFace introduces three methods: Face Recognition Consistency for refined demographic labels, Face Vendi Score Guidance for improved interclass diversity, and Divergence Score Conditioning to balance identity preservation and intraclass diversity. The results show that VariFace outperforms previous synthetic datasets (0.9200 → 0.9405) and achieves comparable performance to real data-trained models (Real Gap = -0.0065). In an unconstrained setting, VariFace sets a new state-of-the-art performance with an average face verification accuracy of 0.9567 (Real Gap = +0.0097) across six evaluation datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in face recognition technology. Right now, using big websites to train face recognition models can be bad because it can invade people’s privacy and be biased towards certain groups. To fix this, the researchers created a new way to make fake faces that are more diverse and work better than existing methods. They call this method VariFace. It has three parts: one helps with labels, one makes the fake faces look more different from each other, and one makes sure the faces don’t get too similar or too different. The results show that VariFace works really well, even better than using real data. |
Keywords
» Artificial intelligence » Diffusion » Face recognition
