为脆弱人群的生物识别系统设计轻量级安全模板保护方案
Securing Face and Fingerprint Templates in Humanitarian Biometric Systems
- 用PolyProtect保护面部和指纹嵌入特征，实现不可逆与不可链接
- 在埃塞俄比亚实地数据集上验证，识别准确率损失小于1.5%
- 首个在指纹与识别场景中测试PolyProtect的研究，适合人道项目使用
在人道主义与紧急情境中，生物识别可显著提升运营效率，但对数据主体构成风险，尤其在弱势群体中更为突出。为此，我们提出一种适用于此类场景的移动生物识别系统，采用生物特征模板保护（BTP）方案。在严格定义功能、操作及安全隐私需求后，对现有BTP方法进行全面比较。发现针对神经网络面部嵌入设计的PolyProtect最具适用性，因其高效、模块化且计算开销小。我们在埃塞俄比亚人道主义项目的真实面部数据集上，评估了其在边缘人脸特征提取器EdgeFace生成的嵌入上的表现，涵盖验证与识别准确率、不可逆性与不可链接性。此外，由于PolyProtect具有模态无关性，我们首次将其扩展至指纹评估。实验结果表明其性能优异，代码计划开源。
原文摘要 · Abstract (English)
In humanitarian and emergency scenarios, the use of biometrics can dramatically improve the efficiency of operations, but it poses risks for the data subjects, which are exacerbated in contexts of vulnerability. To address this, we present a mobile biometric system implementing a biometric template protection (BTP) scheme suitable for these scenarios. After rigorously formulating the functional, operational, and security and privacy requirements of these contexts, we perform a broad comparative analysis of the BTP landscape. PolyProtect, a method designed to operate on neural network face embeddings, is identified as the most suitable method due to its effectiveness, modularity, and lightweight computational burden. We evaluate PolyProtect in terms of verification and identification accuracy, irreversibility, and unlinkability, when this BTP method is applied to face embeddings extracted using EdgeFace, a novel state-of-the-art efficient feature extractor, on a real-world face dataset from a humanitarian field project in Ethiopia. Moreover, as PolyProtect promises to be modality-independent, we extend its evaluation to fingerprints. To the best of our knowledge, this is the first time that PolyProtect has been evaluated for the identification scenario and for fingerprint biometrics. Our experimental results are promising, and we plan to release our code
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