# Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching

arXiv: https://arxiv.org/abs/2601.02318 · HTML: https://mlsanity.com/paper/2601.02318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects](https://mlsanity.com/paper/2103.03038/) (2021)
- [RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset](https://mlsanity.com/paper/2307.05563/) (2023)

## Built on by (most cited first, 1 of 1)

- [Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging](https://mlsanity.com/paper/2603.17679/) (2026)

## Abstract (arXiv)

Contactless fingerprint recognition offers a hygienic and convenient alternative to contact-based systems, enabling rapid acquisition without latent prints, pressure artifacts, or hygiene risks. However, contactless images often show degraded ridge clarity due to illumination variation, subcutaneous skin discoloration, and specular reflections. Flash captures preserve ridge detail but introduce noise, whereas non-flash captures reduce noise but lower ridge contrast. We propose Fusion2Print (F2P), the first framework to systematically capture and fuse paired flash-non-flash contactless fingerprints. We construct a custom paired dataset, FNF Database, and perform manual flash-non-flash subtraction to isolate ridge-preserving signals. A lightweight attention-based fusion network also integrates both modalities, emphasizing informative channels and suppressing noise, and then a U-Net enhancement module produces an optimally weighted grayscale image. Finally, a deep embedding model with cross-domain compatibility, generates discriminative and robust representations in a unified embedding space compatible with both contactless and contact-based fingerprints for verification. F2P enhances ridge clarity and achieves superior recognition performance (AUC=0.999, EER=1.12%) over single-capture baselines (Verifinger, DeepPrint).