Abstract
Two-dimensional pose estimation plays a crucial role in fingerprint recognition by facilitating global alignment and reduce pose-induced variations. However, existing methods are still unsatisfactory when handling with large angle or small area inputs. These limitations are particularly pronounced on fingerprints captured by under-screen fingerprint sensors in smartphones. In this paper, we present a novel dual-modal input based network for under-screen fingerprint pose estimation. Our approach effectively integrates two distinct yet complementary modalities: texture details extracted from ridge patches through the under-screen fingerprint sensor, and rough contours derived from capacitive images obtained via the touch screen. This collaborative integration endows our network with more comprehensive and discriminative information, substantially improving the accuracy and stability of pose estimation. A decoupled probability distribution prediction task is designed, instead of the traditional supervised forms of numerical regression or heatmap voting, to facilitate the training process. Additionally, we incorporate a Mixture of Experts (MoE) based feature fusion mechanism and a relationship driven cross-domain knowledge transfer strategy to further strengthen feature extraction and fusion capabilities. Extensive experiments are conducted on several public datasets and two private datasets. The results indicate that our method is significantly superior to previous state-of-the-art (SOTA) methods and remarkably boosts the recognition ability of fingerprint recognition algorithms. Our code is available at this https URL.
Abstract (translated)
二维姿态估计在指纹识别中扮演着重要角色，通过促进全局对齐并减少姿态引起的变异来提高系统的性能。然而，现有的方法在处理大角度或小区域输入时仍不尽如人意。这些限制尤其体现在智能手机下屏指纹传感器捕获的指纹图像上。 本文提出了一种基于双模态输入的新网络架构，专门用于手机屏幕下方指纹的姿态估计。我们的方法有效地融合了两种互补的信息模式：一种是通过下屏指纹传感器提取的纹理细节（即从脊纹区域获取），另一种是从触控屏幕获得的电容图像中得出的大致轮廓。这种协作式的集成使我们的网络能够利用更全面和具有区分性的信息，从而显著提高姿态估计的准确性和稳定性。 我们设计了一个解耦的概率分布预测任务，而非传统的数值回归或热图投票方法，以促进训练过程。此外，还引入了一种基于专家混合（MoE）的特征融合机制以及一种驱动关系的跨域知识转移策略，进一步增强了特征提取和融合的能力。 我们在多个公开数据集和两个私有数据集上进行了广泛的实验。结果显示，我们的方法显著优于现有的最先进方法，并大幅提升了指纹识别算法的识别能力。 代码可在以下网址获取：[此处应为URL，原文中未提供具体链接，请访问相关论文或项目页面获取]
URL
https://arxiv.org/abs/2505.02481
