  Fingerprint dense registration aims to finely align fingerprint pairs at the
pixel level, thereby reducing intra-class differences caused by distortion.
Unfortunately, traditional methods exhibited subpar performance when dealing
with low-quality fingerprints while suffering from slow inference speed.
Although deep learning based approaches shows significant improvement in these
aspects, their registration accuracy is still unsatisfactory. In this paper, we
propose a Phase-aggregated Dual-branch Registration Network (PDRNet) to
aggregate the advantages of both types of methods. A dual-branch structure with
multi-stage interactions is introduced between correlation information at high
resolution and texture feature at low resolution, to perceive local fine
differences while ensuring global stability. Extensive experiments are
conducted on more comprehensive databases compared to previous works.
Experimental results demonstrate that our method reaches the state-of-the-art
registration performance in terms of accuracy and robustness, while maintaining
considerable competitiveness in efficiency.
