Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging

TL;DR

This paper proposes a novel illumination-aware approach using paired flash and non-flash imaging to improve contactless fingerprint spoof detection, analyzing lighting-induced differences for better robustness.

Contribution

It introduces a paired flash-non-flash acquisition method and interpretable metrics to enhance spoof detection in contactless fingerprint recognition.

Findings

Flash illumination accentuates material-dependent properties.

Differential imaging helps discriminate genuine from spoof fingerprints.

Analysis reveals limitations related to imaging settings and high-fidelity spoofs.

Abstract

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.