# Regression of Dense Distortion Field from a Single Fingerprint Image

arXiv: https://arxiv.org/abs/2404.17610 · HTML: https://mlsanity.com/paper/2404.17610/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network](https://mlsanity.com/paper/2004.05972/) (2020)
- [Fingerprint Distortion Rectification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1801.01198/) (2018)
- [Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN](https://mlsanity.com/paper/2008.11917/) (2020)

## Built on by (most cited first, 6 of 6)

- [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)
- [A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching](https://mlsanity.com/paper/2507.16201/) (2025)
- [Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion](https://mlsanity.com/paper/2605.15796/) (2026)
- [Toward the Whole Picture: Accumulative Fingerprint Mapping and Reconstruction for Small-Area Mobile Sensors](https://mlsanity.com/paper/2606.15574/) (2026)
- [UoU: A Universal Fingerprint Foundation Model Based on Large-Scale Unsupervised Learning](https://mlsanity.com/paper/2606.17436/) (2026)

## Abstract (arXiv)

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses and distortion patterns. We conducted experiments on FVC2004 DB1A, expanded Tsinghua Distorted Fingerprint database (with additional distorted fingerprints in diverse finger poses and distortion patterns) and a latent fingerprint database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.