Abstract
Synthetic data has emerged as a promising alternative for training face recognition (FR) models, offering advantages in scalability, privacy compliance, and potential for bias mitigation. However, critical questions remain on whether both high accuracy and fairness can be achieved with synthetic data. In this work, we evaluate the impact of synthetic data on bias and performance of FR systems. We generate balanced face dataset, FairFaceGen, using two state of the art text-to-image generators, Flux.1-dev and Stable Diffusion v3.5 (SD35), and combine them with several identity augmentation methods, including Arc2Face and four IP-Adapters. By maintaining equal identity count across synthetic and real datasets, we ensure fair comparisons when evaluating FR performance on standard (LFW, AgeDB-30, etc.) and challenging IJB-B/C benchmarks and FR bias on Racial Faces in-the-Wild (RFW) dataset. Our results demonstrate that although synthetic data still lags behind the real datasets in the generalization on IJB-B/C, demographically balanced synthetic datasets, especially those generated with SD35, show potential for bias mitigation. We also observe that the number and quality of intra-class augmentations significantly affect FR accuracy and fairness. These findings provide practical guidelines for constructing fairer FR systems using synthetic data.
Abstract (translated)
合成数据已成为训练面部识别（FR）模型的有前途的替代方案，它在可扩展性、隐私合规性和潜在的偏见缓解方面具有优势。然而，关于是否可以通过合成数据同时实现高准确率和公平性的关键问题仍然存在。在这项工作中，我们评估了合成数据对面部识别系统中的偏差和性能的影响。我们使用两个最先进的文本到图像生成器（Flux.1-dev 和 Stable Diffusion v3.5 (SD35)）生成了一个平衡的面部数据集FairFaceGen，并结合了几种身份增强方法，包括Arc2Face和四个IP-Adapters。通过在合成数据集和真实数据集中保持相同的身份计数，我们在评估标准（LFW、AgeDB-30等）和具有挑战性的IJB-B/C基准测试以及使用RFW数据集进行的面部识别偏见时确保了公平比较。 我们的结果表明，尽管在IJB-B/C上的泛化能力方面合成数据仍落后于真实数据集，但人口统计学平衡的合成数据集（尤其是用SD35生成的数据集）在减轻偏见方面显示出潜力。我们还观察到，类内增强的数量和质量显著影响面部识别的准确率和公平性。这些发现为使用合成数据构建更公正的面部识别系统提供了实用指南。
URL
https://arxiv.org/abs/2507.20782
