  One of the open issues in fingerprint verification is the lack of robustness
against image-quality degradation. Poor-quality images result in spurious and
missing features, thus degrading the performance of the overall system.
Therefore, it is important for a fingerprint recognition system to estimate the
quality and validity of the captured fingerprint images. In this work, we
review existing approaches for fingerprint image-quality estimation, including
the rationale behind the published measures and visual examples showing their
behavior under different quality conditions. We have also tested a selection of
fingerprint image-quality estimation algorithms. For the experiments, we employ
the BioSec multimodal baseline corpus, which includes 19200 fingerprint images
from 200 individuals acquired in two sessions with three different sensors. The
behavior of the selected quality measures is compared, showing high correlation
between them in most cases. The effect of low-quality samples in the
verification performance is also studied for a widely available minutiae-based
fingerprint matching system.
