Closing the Performance Gap in Biometric Cryptosystems: A Deeper Analysis on Unlinkable Fuzzy Vaults

TL;DR

This paper introduces a feature quantization method to stabilize fuzzy vault biometric cryptosystems, reducing performance gaps caused by variable feature sets and transformations, and demonstrating effectiveness across multiple biometric modalities.

Contribution

A novel equal frequent intervals feature quantization technique that ensures fixed feature set sizes and adapts without training, improving fuzzy vault system robustness.

Findings

Significantly reduces performance gap in biometric cryptosystems

Supports seamless integration with existing systems

Effective across face, fingerprint, and iris recognition

Abstract

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.