  Contactless fingerprint matching using smartphone cameras can alleviate major
challenges of traditional fingerprint systems including hygienic acquisition,
portability and presentation attacks. However, development of practical and
robust contactless fingerprint matching techniques is constrained by the
limited availability of large scale real-world datasets. To motivate further
advances in contactless fingerprint matching across sensors, we introduce the
RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless
and contact-based fingerprint image pairs acquired from 88 individuals under
different background and lighting conditions using two smartphone cameras and
one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to
promote research under different matching scenarios that include Single Finger
Matching and Multi-Finger Matching for both contactless- to-contactless (CL2CL)
and contact-to-contactless (C2CL) verification and identification. Furthermore,
due to the high intra-sample variance in contactless fingerprints belonging to
the same finger, we propose a set-based matching protocol inspired by the
advances in facial recognition datasets. This protocol is specifically designed
for pragmatic contactless fingerprint matching that can account for variances
in focus, polarity and finger-angles. We report qualitative and quantitative
baseline results for different protocols using a COTS fingerprint matcher
(Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The
dataset can be downloaded here:
https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html
