  An essential factor to achieve high accuracies in fingerprint recognition
systems is the quality of its samples. Previous works mainly proposed
supervised solutions based on image properties that neglects the minutiae
extraction process, despite that most fingerprint recognition techniques are
based on detected minutiae. Consequently, a fingerprint image might be assigned
a high quality even if the utilized minutia extractor produces unreliable
information. In this work, we propose a novel concept of assessing minutia and
fingerprint quality based on minutia detection confidence (MiDeCon). MiDeCon
can be applied to an arbitrary deep learning based minutia extractor and does
not require quality labels for learning. We propose using the detection
reliability of the extracted minutia as its quality indicator. By combining the
highest minutia qualities, MiDeCon also accurately determines the quality of a
full fingerprint. Experiments are conducted on the publicly available databases
of the FVC 2006 and compared against several baselines, such as NIST's
widely-used fingerprint image quality software NFIQ1 and NFIQ2. The results
demonstrate a significantly stronger quality assessment performance of the
proposed MiDeCon-qualities as related works on both, minutia- and
fingerprint-level. The implementation is publicly available.
