mlsanityData to 14 Oct 2026

FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

arXiv 2311.10476 ↗First posted 17 Nov 2023cs.CVcited by 21

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Abstract (arXiv)

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 32 of its 56 references that are in the map. First posted on arXiv 17 Nov 2023.

Builds on

32 of 56 references.

References · 32

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20 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 11; 2026: 2.

21 papers.

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  1. 2024How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainabilitycited by 34
  2. 2024SDFR: Synthetic Data for Face Recognition Competitioncited by 28
  3. 202550 Years of Automated Face Recognitioncited by 15
  4. 2024Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Datacited by 13
  5. 2024HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hyperspherecited by 12
  6. 2025Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusioncited by 12
  7. 2024Digi2Real: Bridging the Realism Gap in Synthetic Data Face Recognition via Foundation Modelscited by 9
  8. 2025Review of Demographic Fairness in Face Recognitioncited by 7
  9. 2024If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Facescited by 4
  10. 2025MINT-Demo: Membership Inference Test Demonstratorcited by 3
  11. 2024Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometricscited by 2
  12. 2025Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometricscited by 2
9 more
  1. 2025Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Datacited by 2
  2. 2025Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Facescited by 1
  3. 2025Vec2Face+ for Face Dataset Generationcited by 1
  4. 2024TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Facescited by 0
  5. 2025Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognitioncited by 0
  6. 2025Enhancing Domain Diversity in Synthetic Data Face Recognition with Dataset Fusioncited by 0
  7. 2025A Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognitioncited by 0
  8. 2026On Applicability of Synthetic Datasets for Facial Expression Recognitioncited by 0
  9. 2026IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Datacited by 0