  In this work, we investigate if previously proposed CNNs for fingerprint pore
detection overestimate the number of required model parameters for this task.
We show that this is indeed the case by proposing a fully convolutional neural
network that has significantly fewer parameters. We evaluate this model using a
rigorous and reproducible protocol, which was, prior to our work, not available
to the community. Using our protocol, we show that the proposed model, when
combined with post-processing, performs better than previous methods, albeit
being much more efficient. All our code is available at
https://github.com/gdahia/fingerprint-pore-detection
