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

arXiv: https://arxiv.org/abs/2603.17679 · HTML: https://mlsanity.com/paper/2603.17679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Presentation Attack Detection with Advanced CNN Models for Noncontact-based Fingerprint Systems](https://mlsanity.com/paper/2303.05459/) (2023)
- [A Comparative Study of Fingerprint Image-Quality Estimation Methods](https://mlsanity.com/paper/2111.07432/) (2021)
- [RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset](https://mlsanity.com/paper/2307.05563/) (2023)
- [Moir'e Attack (MA): A New Potential Risk of Screen Photos](https://mlsanity.com/paper/2110.10444/) (2021)
- [A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems](https://mlsanity.com/paper/2310.15044/) (2023)
- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)
- [Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis](https://mlsanity.com/paper/2403.04038/) (2024)
- [Local Binary Pattern(LBP) Optimization for Feature Extraction](https://mlsanity.com/paper/2407.18665/) (2024)
- [GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2504.01213/) (2025)
- [RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection](https://mlsanity.com/paper/1708.07887/) (2017)
- [Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching](https://mlsanity.com/paper/2601.02318/) (2026)

## Abstract (arXiv)

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.