Small-area fingerprint sensing on mobile devices creates a fundamental mismatch between acquisition and recognition: each touch captures only a tiny, pose-varying local patch, while reliable biometric matching ultimately requires a stable and sufficiently complete fingerprint representation. Existing pipelines largely cope with this mismatch by treating repeated touches as independent partial templates, which leads to repeated registration, repeated matching, and no guarantee of adequate global coverage. In this paper, we advocate a different formulation, namely \emph{accumulative fingerprint mapping and reconstruction} for small-area mobile sensing. Rather than matching every partial patch separately, the proposed perspective converts a sequence of local observations into a unified fingerprint state that is progressively refined as new touches arrive and can be matched only once after consolidation. As a concrete baseline, we present a classical pipeline that performs patch-wise structural feature extraction, feature-level registration and fusion, fingerprint map construction, and phase-based ridge reconstruction. More importantly, we position this baseline within a broader mobile fingerprint framework that integrates structured token learning, two-stage pose reasoning, and diffusion-based generative reconstruction. This viewpoint reframes mobile fingerprint recognition from multi-capture multi-match processing to accumulative map building, state refinement, and one-shot matching, offering a principled route toward efficient, pose-robust, and deployment-friendly biometrics for small-area mobile platforms. The baseline implementation has been publicly released at https://github.com/XiongjunGuan/FpReconstruction.

翻译：小面积指纹传感在移动设备上造成了采集与识别之间的根本性不匹配：每次触控仅捕获一个微小且姿态变化的局部区域，而可靠的生物识别匹配最终需要稳定且足够完整的指纹表征。现有流程主要通过将重复触控视为独立的局部模板来应对这种不匹配，这导致反复注册、反复匹配，且无法保证充分的全局覆盖。本文倡导一种不同的方法，即面向小面积移动传感的“累积式指纹图谱构建与重建”。所提出的视角并非对每个局部区域进行单独匹配，而是将一系列局部观测转化为统一的指纹状态，该状态随新的触控输入而逐步精化，并在合并后可仅进行一次匹配。作为具体基线，我们提出了一套经典流程，包含局部块结构特征提取、特征级配准与融合、指纹图谱构建以及基于相位的脊线重建。更重要的是，我们将该基线放置于更广泛的移动指纹框架中，该框架整合了结构化令牌学习、两阶段姿态推理以及基于扩散的生成式重建。这一视角将移动指纹识别从多次采集、多次匹配的处理方式重构为累积式图谱构建、状态精化与单次匹配，为小面积移动平台提供了一条实现高效、姿态鲁棒且易于部署的生物识别原则性路径。该基线实现已在https://github.com/XiongjunGuan/FpReconstruction公开。