  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%).
