Paper Info
Title
C2CL: Contact to Contactless 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%).
Year
DOI
Venue
2022
10.1109/TIFS.2021.3134867
IEEE Transactions on Information Forensics and Security
Keywords
DocType
Volume
Fingerprint recognition,sensor interoperability,contact to contactless fingerprint matching
Journal
17
ISSN
Citations 
PageRank 
1556-6013
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Steven A. Grosz100.34
Joshua J. Engelsma2225.78
Eryun Liu313811.46
Anil Jain4335073334.84