  With the emergence of high-resolution fingerprint sensors, there has been a
lot of focus on level-3 fingerprint features, especially the pores, for the
next generation automated fingerprint recognition systems (AFRS). Following the
success of deep learning in various computer vision tasks, researchers have
developed learning-based approaches for detection of pores in high-resolution
fingerprint images. Generally, learning-based approaches provide better
performance than handcrafted feature-based approaches. However, domain
adaptability of the existing learning-based pore detection methods has never
been studied. In this paper, we study this aspect and propose an approach for
pore detection in cross-sensor scenarios. For this purpose, we have generated
an in-house 1000 dpi fingerprint dataset with ground truth pore coordinates
(referred to as IITI-HRFP-GT), and evaluated the performance of the existing
learning-based pore detection approaches. The core of the proposed approach for
detection of pores in cross-sensor scenarios is DeepDomainPore, which is a
residual learning-based convolutional neural network(CNN) trained for pore
detection. The domain adaptability in DeepDomainPore is achieved by embedding a
gradient reversal layer between the CNN and a domain classifier network. The
proposed approach achieves state-of-the-art performance in a cross-sensor
scenario involving public high-resolution fingerprint datasets with 88.12% true
detection rate and 83.82% F-score.
