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

arXiv: https://arxiv.org/abs/2606.15574 · HTML: https://mlsanity.com/paper/2606.15574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 14 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images](https://mlsanity.com/paper/2106.12859/) (2021)
- [RecDiffusion: Rectangling for Image Stitching with Diffusion Models](https://mlsanity.com/paper/2403.19164/) (2024)
- [Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks](https://mlsanity.com/paper/2402.15959/) (2024)
- [Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration](https://mlsanity.com/paper/2404.17159/) (2024)
- [Regression of Dense Distortion Field from a Single Fingerprint Image](https://mlsanity.com/paper/2404.17610/) (2024)
- [Joint Identity Verification and Pose Alignment for Partial Fingerprints](https://mlsanity.com/paper/2405.03959/) (2024)
- [Finger Pose Estimation for Under-screen Fingerprint Sensor](https://mlsanity.com/paper/2505.02481/) (2025)
- [Implicit Neural Image Stitching](https://mlsanity.com/paper/2309.01409/) (2023)

## Abstract (arXiv)

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 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.