Toward the Whole Picture: Accumulative Fingerprint Mapping and Reconstruction for Small-Area Mobile Sensors

2026-06-13

AI Summary

This work addresses the challenge of constructing stable representations from partial and highly variable fingerprint fragments captured by small-area mobile sensors. The authors propose a cumulative fingerprint mapping and reconstruction framework that reframes mobile fingerprint recognition into a novel paradigm: “cumulative mapping—state optimization—single-shot matching.” By integrating local structural feature extraction, feature-level registration, and phase-based ridge reconstruction, and further incorporating structured token learning, two-stage pose inference, and diffusion-based generation, the method achieves efficient and robust fingerprint identification. The system substantially enhances pose robustness and deployment compatibility, offering a practical biometric solution for resource-constrained devices. The implementation has been open-sourced to facilitate broader adoption and reproducibility.

Abstract

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.

Problem

• fingerprint sensing
• small-area sensors
• mobile biometrics
• partial fingerprint
• global coverage

Innovation

• accumulative fingerprint mapping
• small-area sensing
• pose-robust biometrics
• diffusion-based reconstruction
• one-shot matching