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.

提出了一种相位聚合的双分支注册网络（PDRNet），通过在高分辨率的相关信息和低分辨率的纹理特征之间引入双分支结构与多阶段交互，在感知局部细微差异的同时保证全局稳定，从而聚合了传统方法和基于深度学习的方法的优势，实验证明了在准确性和鲁棒性上达到了最先进的注册性能，同时在效率上也具有相当的竞争力。