Abstract
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.
Abstract (translated)
目前，便携式电子设备越来越受欢迎。考虑到轻量化的因素，它们的指纹识别模块通常使用有限尺寸的传感器。然而，部分指纹具有有限的可匹配特征，尤其是在手指按压姿势或图像质量存在差异时，这使得部分指纹验证具有挑战性。大多数现有方法将指纹位置校正和身份验证视为独立的任务，忽略了它们之间的耦合关系——相对姿态估计通常依赖于成对特征作为锚点，而身份验证准确性往往随着更精确的姿势对齐而提高。因此，在本文中，我们提出了一个方法，该方法共同估计部分指纹的身份验证和相对姿态，旨在利用它们固有的相关性提高彼此。为达到这一目标，我们提出了一个多任务 CNN-Transformer 混合网络，并设计了一个预训练任务来增强特征提取能力。在多个公开数据集（NIST SD14，FVC2002 DB1A & DB3A，FVC2004 DB1A & DB2A，FVC2006 DB1A & DB2A）和内部数据集的实验结果表明，我们的方法在部分指纹验证和相对姿态估计方面实现了最先进的性能，而效率比以前的方法更高。
URL
https://arxiv.org/abs/2405.03959
