  Fingerprint recognition plays an important role in many commercial
applications and is used by millions of people every day, e.g. for unlocking
mobile phones. Fingerprint image segmentation is typically the first processing
step of most fingerprint algorithms and it divides an image into foreground,
the region of interest, and background. Two types of error can occur during
this step which both have a negative impact on the recognition performance:
'true' foreground can be labeled as background and features like minutiae can
be lost, or conversely 'true' background can be misclassified as foreground and
spurious features can be introduced. The contribution of this paper is
threefold: firstly, we propose a novel factorized directional bandpass (FDB)
segmentation method for texture extraction based on the directional Hilbert
transform of a Butterworth bandpass (DHBB) filter interwoven with
soft-thresholding. Secondly, we provide a manually marked ground truth
segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a
systematic performance comparison between the FDB method and four of the most
often cited fingerprint segmentation algorithms showing that the FDB
segmentation method clearly outperforms these four widely used methods. The
benchmark and the implementation of the FDB method are made publicly available.
