Fingerprint recognition is still dominated by task-specific pipelines, where enhancement, structural parsing, alignment, and matching are optimized in isolation. Although effective in narrow settings, this design limits representation reuse across sensors, qualities, and downstream applications. We therefore present UoU, short for ``a \textbf{U}niversal fingerprint foundation model based \textbf{o}n large-scale \textbf{U}nsupervised learning,'' which reframes fingerprint feature extraction as a domain-specific foundation-model problem. UoU is organized around a multi-level representation hierarchy spanning image restoration, structural fields, semantic tokens, point-level biometric entities, and compact global descriptors. Its training recipe combines a supervised cold start on precise annotations, large-scale weakly supervised refinement, and large-scale unsupervised consolidation, with the latter two stages iterated during large-scale training so that weak supervision broadens semantic coverage while unsupervised learning stabilizes correspondences, invariances, and representation geometry. Rather than treating fingerprint imagery as generic texture, UoU exploits domain-specific symmetries and intermediate structure, including orientation flow, periodic ridge patterns, sparse biometric entities, and spatial equivariance. The framework is intentionally architecture-agnostic: while the present study includes an initial transformer-based structured-prediction instantiation, the broader design supports multi-task learning, scalable model configurations, and downstream specialization for matching, alignment, enhancement, registration, and related fingerprint applications. This paper presents the technical motivation, system design, and validation protocol of UoU, and part of the baseline implementation is publicly available at https://github.com/XiongjunGuan/UoU.

翻译：指纹识别仍以任务专用流程为主导，其中增强、结构解析、比对和匹配等步骤被独立优化。尽管在特定场景中表现有效，但这种设计限制了表征在不同传感器、图像质量及下游应用间的复用性。为此，我们提出UoU（全称"基于大规模无监督学习的通用指纹基础模型"），将指纹特征提取重构为领域专用的基础模型问题。UoU围绕多层级表征层级体系展开，涵盖图像修复、结构场、语义标记、点级生物特征实体及紧凑型全局描述符。其训练策略结合了基于精确标注的监督冷启动、大规模弱监督精调及大规模无监督整合，其中后两个阶段在大规模训练中迭代进行，使弱监督拓展语义覆盖范围，而无监督学习则稳定对应关系、不变性及表征几何结构。UoU未将指纹图像视为通用纹理，而是充分利用领域特有的对称性及中间结构，包括方向流、周期脊模式、稀疏生物特征实体及空间等变性。该框架刻意保持架构无关性：尽管本研究包含初始基于Transformer的结构预测实例，但整体设计支持多任务学习、可扩展模型配置，以及面向匹配、比对、增强、配准及相关指纹应用的下游专精化。本文阐述UoU的技术动机、系统设计与验证协议，部分基线实现已公开于https://github.com/XiongjunGuan/UoU。