# Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network

arXiv: https://arxiv.org/abs/2004.05972 · HTML: https://mlsanity.com/paper/2004.05972/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (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)
- [Direct Regression of Distortion Field from a Single Fingerprint Image](https://mlsanity.com/paper/2404.17148/) (2024)
- [A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching](https://mlsanity.com/paper/2507.16201/) (2025)
- [Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2501.05034/) (2025)
- [FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting](https://mlsanity.com/paper/2509.15648/) (2025)
- [Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion](https://mlsanity.com/paper/2605.15796/) (2026)

## Abstract (arXiv)

Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern. To overcome the limitation of handcraft features, we propose to train an end-to-end network to directly output pixel-wise displacement field between two fingerprints. The proposed network includes a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. By applying displacement fields reliably estimated by tracing high quality fingerprint videos to challenging fingerprints, we synthesize a large number of training fingerprint pairs with ground truth displacement fields. In addition, based on the proposed registration algorithm, we propose a fingerprint mosaicking method based on optimal seam selection. Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous dense registration methods in accuracy and efficiency. Mosaicking experiment on FVC2004 DB1 demonstrates that the proposed algorithm produced higher quality fingerprints than other algorithms which also validates the performance of our registration algorithm.