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

Roja Sahoo, Anoop Namboodiri

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.

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

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.
Paper Structure (10 sections, 7 figures, 3 tables)

This paper contains 10 sections, 7 figures, 3 tables.

Table of Contents

  1. Introduction and Motivation
  2. Background and Related Work
  3. Empirical Observations Under Controlled Illumination
  4. Photometric and Quality Variations Across Lighting Conditions
  5. Model Interpretability Under Illumination Change
  6. Discussion
  7. Presentation Attack Scenarios
  8. Implications for Spoof Detection Design
  9. Limitations and Challenges
  10. Conclusion

Figures (7)

  • Figure 1: Blockwise OCL maps for sample flash and non-flash fingerprint images. Brighter regions(higher intensity) indicate higher ridge clarity; flash images show consistently stronger ridge coherence.
  • Figure 2: LCS and ridge-valley intensity profiles for sample flash and non-flash fingerprint blocks, showing improved ridge-valley contrast under flash illumination.
  • Figure 3: Comparison of standard (AIT Sharpness, NFIQ2) and custom patch-wise image quality metrics over the sample dataset, where flash images outperform non-flash images across all measures.
  • Figure 4: Photometric analysis of local contrast and edge energy for R, G, and B channels of the sample dataset under flash and non-flash illumination.
  • Figure 5: Attention maps for sample flash and non-flash contactless fingerprint images. (a)–(b) Baseline DINOv2 attention maps with limited ridge awareness but improved spatial structure under flash lighting. (c)–(d) Fine-tuned ResNet-18–based model showing enhanced ridge-focused attention, especially for flash images.
  • ...and 2 more figures