Currently, portable electronic devices are becoming more and more popular.
For lightweight considerations, their fingerprint recognition modules usually
use limited-size sensors. However, partial fingerprints have few matchable
features, especially when there are differences in finger pressing posture or
image quality, which makes partial fingerprint verification challenging. Most
existing methods regard fingerprint position rectification and identity
verification as independent tasks, ignoring the coupling relationship between
them -- relative pose estimation typically relies on paired features as
anchors, and authentication accuracy tends to improve with more precise pose
alignment. Consequently, in this paper we propose a method that jointly
estimates identity verification and relative pose for partial fingerprints,
aiming to leverage their inherent correlation to improve each other. To achieve
this, we propose a multi-task CNN (Convolutional Neural Network)-Transformer
hybrid network, and design a pre-training task to enhance the feature
extraction capability. Experiments on multiple public datasets (NIST SD14,
FVC2002 DB1A & DB3A, FVC2004 DB1A & DB2A, FVC2006 DB1A) and an in-house dataset
show that our method achieves state-of-the-art performance in both partial
fingerprint verification and relative pose estimation, while being more
efficient than previous methods.

基于多任务 CNN-Transformer 混合网络，本研究提出了一种联合估计部分指纹的身份验证和相对位姿的方法，旨在利用它们之间的固有相关性相互改进，实验结果表明，该方法在部分指纹验证和相对位姿估计方面取得了最先进的性能。