解决生物特征加密系统中的性能差距问题
Closing the Performance Gap in Biometric Cryptosystems: A Deeper Analysis on Unlinkable Fuzzy Vaults
- 采用等频区间量化方法固定特征集大小
- 显著降低模板保护带来的性能损失，仅剩微小退化
- 无需训练即可适配任意区间数，兼容现有系统
本文分析并解决了基于模糊保险库的生物特征加密系统（BCS）中的性能差距问题。我们识别出错误校正能力不稳定是主因，源于特征集大小变化及其对相似度阈值的影响，同时特征类型转换也引入信息丢失。为此，提出一种基于等频区间的新型特征量化方法，可保证固定特征集大小，并支持无需训练地适配任意区间数。该方法显著缩小了模板保护造成的性能差距，且与现有系统无缝集成，有效缓解特征转换的负面影响。在主流人脸、指纹和虹膜识别系统上的实验表明，仅存在极小性能退化，证明了该方法在主要生物特征模态上的有效性。
原文摘要 · Abstract (English)
This paper analyses and addresses the performance gap in the fuzzy vault-based \ac{BCS}. We identify unstable error correction capabilities, which are caused by variable feature set sizes and their influence on similarity thresholds, as a key source of performance degradation. This issue is further compounded by information loss introduced through feature type transformations. To address both problems, we propose a novel feature quantization method based on \it{equal frequent intervals}. This method guarantees fixed feature set sizes and supports training-free adaptation to any number of intervals. The proposed approach significantly reduces the performance gap introduced by template protection. Additionally, it integrates seamlessly with existing systems to minimize the negative effects of feature transformation. Experiments on state-of-the-art face, fingerprint, and iris recognition systems confirm that only minimal performance degradation remains, demonstrating the effectiveness of the method across major biometric modalities.
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