发表机构
Faculty of Information Technology and Electrical Engineering, University of Oulu（奥卢大学信息技术与电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究合成数据用于边境控制系统人脸识别阈值校准，分析合成与真实数据集分数分布对齐及校准阈值跨域转移性，发现合成数据在受控环境可近似校准，但在无约束条件下不可靠，校准结果依赖数据集，高安全部署需真实数据验证调整。
AI 中文摘要
最近部署的出入境系统（EES）将大规模生物特征验证引入欧洲边境管制，要求人脸识别系统在极低的误匹配率（FMR）下运行。监管框架在EES中央系统层面定义了性能目标，但未规定成员国层面实际校准验证阈值的方法。在实际操作中，获取用于校准的代表性真实世界数据常受法律、后勤和隐私限制。本文研究合成人脸数据在与边境控制系统相关的文档到活体验证场景中的阈值校准应用。分析合成数据集与真实数据集之间真实和冒名顶替者分数分布的对齐情况，并评估校准阈值在不同域间的可转移性，重点关注低FMR工作点。结果表明，合成数据在受控环境中可近似校准行为，但由于分数分布尾部不匹配，无法可靠地推广到无约束条件。这些差异导致识别性能显著下降，且易受基于变形的攻击。进一步证明校准结果高度依赖数据集，即使是合成数据集。总体而言，研究结果表明，虽然合成数据对系统开发和初步校准有用，但在高安全部署中，可靠的阈值选择通常需要使用代表性真实世界数据进行验证和调整。
英文摘要
The recently deployed Entry/Exit System (EES) introduces large-scale biometric verification into European border control, requiring face recognition systems to operate at extremely low false match rates (FMR). While regulatory frameworks define performance targets at the EES Central System level, they do not specify how verification thresholds should be calibrated in practice at the Member State level. In operational settings, obtaining representative real-world data for calibration is often constrained by legal, logistical, and privacy limitations. In this work, we investigate the use of synthetic face data for threshold calibration in document-to-live verification scenarios relevant to border control systems. We analyze the alignment of genuine and impostor score distributions between synthetic and real datasets and evaluate the transferability of calibrated thresholds across domains, with a focus on low-FMR operating points. Our results show that synthetic data can approximate calibration behavior in controlled settings, but fails to reliably generalize to unconstrained conditions due to mismatches in score distribution tails. These discrepancies lead to significant degradation in recognition performance and increased vulnerability to morph-based attacks. We further demonstrate that calibration outcomes are highly dataset-dependent, even across synthetic datasets. Overall, our findings highlight that while synthetic data is useful for system development and preliminary calibration, our results indicate that reliable threshold selection in high-security deployments typically requires validation and adjustment using representative real-world data.
CommentsAccepted by IJCB 2026
