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.

翻译：目前，便携式电子设备日益普及。出于轻量化考虑，其指纹识别模块通常采用有限尺寸的传感器。然而，部分指纹可匹配特征较少，尤其在手指按压姿态或图像质量存在差异时，这使得部分指纹验证颇具挑战性。现有方法大多将指纹位置校正与身份验证视为独立任务，忽略了二者间的耦合关系——相对姿态估计通常依赖配对特征作为锚点，且验证精度往往随姿态对齐精度的提升而提高。为此，本文提出一种联合估计部分指纹身份验证与相对姿态的方法，旨在利用二者内在相关性促进性能互提。为实现这一目标，我们构建了多任务CNN-Transformer混合网络，并设计预训练任务以增强特征提取能力。在多个公开数据集（NIST SD14、FVC2002 DB1A与DB3A、FVC2004 DB1A与DB2A、FVC2006 DB1A）及自建数据集上的实验表明，本方法在部分指纹验证与相对姿态估计任务中均达到最优性能，且效率优于现有方法。