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

TL;DR

Fusion2Print introduces a novel deep learning framework that fuses flash and non-flash contactless fingerprint images to improve recognition accuracy and robustness.

Contribution

It is the first systematic approach to capture and fuse paired flash-non-flash contactless fingerprints, including a new dataset and a specialized fusion network.

Findings

Achieves an AUC of 0.999 and EER of 1.12% in recognition.

Outperforms single-capture baselines like Verifinger and DeepPrint.

Enhances ridge clarity and robustness in contactless fingerprint verification.

Abstract

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).