# Monocular 3D Fingerprint Reconstruction and Unwarping

arXiv: https://arxiv.org/abs/2205.00967 · HTML: https://mlsanity.com/paper/2205.00967/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [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)

## Built on by (most cited first, 11 of 11)

- [Fixed-length Dense Descriptor for Efficient Fingerprint Matching](https://mlsanity.com/paper/2311.18576/) (2023)
- [Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement](https://mlsanity.com/paper/2505.03597/) (2025)
- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)
- [GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2504.01213/) (2025)
- [Recent Advancement in 3D Biometrics using Monocular Camera](https://mlsanity.com/paper/2401.02646/) (2024)
- [Improving Contactless Fingerprint Recognition with Robust 3D Feature Extraction and Graph Embedding](https://mlsanity.com/paper/2409.08782/) (2024)
- [Improving 3D Finger Traits Recognition via Generalizable Neural Rendering](https://mlsanity.com/paper/2410.09582/) (2024)
- [Minutiae-Anchored Local Dense Representation for Fingerprint Matching](https://mlsanity.com/paper/2507.15297/) (2025)
- [FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting](https://mlsanity.com/paper/2509.15648/) (2025)
- [Identity-Consistent Multi-Pose Generation of Contactless Fingerprints](https://mlsanity.com/paper/2605.03830/) (2026)
- [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)

Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, larger fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes ridge orientation, frequency, and minutiae location, and thus causes degraded recognition accuracy. We propose a learning based shape from texture algorithm to reconstruct a 3D finger shape from a single image and unwarp the raw image to suppress perspective distortion. Experimental results on contactless fingerprint databases show that the proposed method has high 3D reconstruction accuracy. Matching experiments on contactless-contact and contactless-contactless matching prove that the proposed method improves matching accuracy.