利用闪光与非闪光成像差异,提升无接触指纹伪造检测的鲁棒性。
Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging
- 通过对比闪光与非闪光图像,捕捉材料与结构特性差异。
- 在多种设备和攻击类型下,检测准确率显著优于单图方法。
- 适合需要高可靠性生物识别的安防与支付场景。
无接触指纹识别虽便捷卫生,但缺乏物理接触和传统活体线索,给伪造检测带来挑战。现有方法多依赖单图外观特征,跨设备、条件和材质泛化能力差。本文研究配对闪光-非闪光成像作为轻量级主动感知机制。实证分析表明,闪光能强化纹路可见性、次表面散射、微结构及表面油脂等材料与结构相关属性,而非闪光图像提供基础外观参照。通过通道间相关性、镜面反射特征、纹理真实性及差分成像等可解释指标分析光照差异,有效区分印刷、数字和模制类伪造攻击。同时探讨了成像设置敏感性、数据集规模及高保真伪造的局限性。结果表明,光照感知分析可显著提升无接触指纹伪造检测的鲁棒性与可解释性,推动未来配对采集与物理启发特征设计的发展。代码已开源。
原文摘要 · Abstract (English)
Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the absence of physical contact and traditional liveness cues. Most existing methods rely on single-image acquisition and appearance-based features, which often generalize poorly across devices, capture conditions, and spoof materials. In this work, we study paired flash-non-flash contactless fingerprint acquisition as a lightweight active sensing mechanism for spoof detection. Through a preliminary empirical analysis, we show that flash illumination accentuates material- and structure-dependent properties, including ridge visibility, subsurface scattering, micro-geometry, and surface oils, while non-flash images provide a baseline appearance context. We analyze lighting-induced differences using interpretable metrics such as inter-channel correlation, specular reflection characteristics, texture realism, and differential imaging. These complementary features help discriminate genuine fingerprints from printed, digital, and molded presentation attacks. We further examine the limitations of paired acquisition, including sensitivity to imaging settings, dataset scale, and emerging high-fidelity spoofs. Our findings demonstrate the potential of illumination-aware analysis to improve robustness and interpretability in contactless fingerprint presentation attack detection, motivating future work on paired acquisition and physics-informed feature design. Code is available in the repository.
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