MCLFIQ: Mobile Contactless Fingerprint Image Quality

Abstract—We propose MCLFIQ: Mobile Contactless Fingerprint
Image Quality, the first quality assessment algorithm for
mobile contactless fingerprint samples. To this end, we retrained
the NIST Fingerprint Image Quality (NFIQ) 2 method,
which was originally designed for contact-based fingerprints,
with a synthetic contactless fingerprint database. We evaluate
the predictive performance of the resulting MCLFIQ model
in terms of Error-vs.-Discard Characteristic (EDC) curves on
three real-world contactless fingerprint databases using two
recognition algorithms. In experiments, the MCLFIQ method is
compared against the original NFIQ 2 method and a sharpnessbased
quality assessment algorithm developed for contactless
fingerprint images. Obtained results show that the re-training
of NFIQ 2 on synthetic data is a viable alternative to training
on real databases. Moreover, the evaluation shows that our
MCLFIQ method works more accurate and robust compared to
NFIQ 2 and the sharpness-based quality assessment. We suggest
considering the proposed MCLFIQ method as a candidate for
a new standard algorithm for contactless fingerprint quality
assessment.