  This letter presents a residual learning-based convolutional neural network,
referred to as DeepResPore, for detection of pores in high-resolution
fingerprint images. Specifically, the proposed DeepResPore model generates a
pore intensity map from the input fingerprint image. Subsequently, the local
maxima filter is operated on the pore intensity map to identify the pore
coordinates. The results of our experiments indicate that the proposed approach
is effective in extracting pores with a true detection rate of 94:49% on Test
set I and 93:78% on Test set II of the publicly available PolyU HRF dataset.
Most importantly, the proposed approach achieves state-of-the-art performance
on both test sets.
