  Typical evaluations of fingerprint recognition systems consist of end-to-end
black-box evaluations, which assess performance in terms of overall
identification or authentication accuracy. However, these black-box tests of
system performance do not reveal insights into the performance of the
individual modules, including image acquisition, feature extraction, and
matching. On the other hand, white-box evaluations, the topic of this paper,
measure the individual performance of each constituent module in isolation.
While a few studies have conducted white-box evaluations of the fingerprint
reader, feature extractor, and matching components, no existing study has
provided a full system, white-box analysis of the uncertainty introduced at
each stage of a fingerprint recognition system. In this work, we extend
previous white-box evaluations of fingerprint recognition system components and
provide a unified, in-depth analysis of fingerprint recognition system
performance based on the aggregated white-box evaluation results. In
particular, we analyze the uncertainty introduced at each stage of the
fingerprint recognition system due to adverse capture conditions (i.e., varying
illumination, moisture, and pressure) at the time of acquisition. Our
experiments show that a system that performs better overall, in terms of
black-box recognition performance, does not necessarily perform best at each
module in the fingerprint recognition system pipeline, which can only be seen
with white-box analysis of each sub-module. Findings such as these enable
researchers to better focus their efforts in improving fingerprint recognition
systems.
