C2CL: Contact to Contactless Fingerprint Matching

TL;DR

This paper introduces C2CL, an end-to-end system for contactless to contact-based fingerprint matching that addresses various acquisition challenges, demonstrating significant accuracy improvements on multiple datasets.

Contribution

The paper presents a novel automated system combining preprocessing and matching algorithms specifically designed for contactless fingerprint verification, improving robustness and accuracy.

Findings

Achieved TAR of 96.67% to 98.30% at FAR=0.01% on multiple datasets.

Developed preprocessing techniques for segmentation, enhancement, and unwarping of contactless fingerprints.

Demonstrated substantial improvement over existing methods in contactless to contact-based fingerprint matching.

Abstract

Matching contactless fingerprints or finger photos to contact-based fingerprint impressions has received increased attention in the wake of COVID-19 due to the superior hygiene of the contactless acquisition and the widespread availability of low cost mobile phones capable of capturing photos of fingerprints with sufficient resolution for verification purposes. This paper presents an end-to-end automated system, called C2CL, comprised of a mobile finger photo capture app, preprocessing, and matching algorithms to handle the challenges inhibiting previous cross-matching methods; namely i) low ridge-valley contrast of contactless fingerprints, ii) varying roll, pitch, yaw, and distance of the finger to the camera, iii) non-linear distortion of contact-based fingerprints, and vi) different image qualities of smartphone cameras. Our preprocessing algorithm segments, enhances, scales, and unwarps contactless fingerprints, while our matching algorithm extracts both minutiae and texture representations. A sequestered dataset of 9,888 contactless 2D fingerprints and corresponding contact-based fingerprints from 206 subjects (2 thumbs and 2 index fingers for each subject) acquired using our mobile capture app is used to evaluate the cross-database performance of our proposed algorithm. Furthermore, additional experimental results on 3 publicly available datasets show substantial improvement in the state-of-the-art for contact to contactless fingerprint matching (TAR in the range of 96.67% to 98.30% at FAR=0.01%).