# FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction

arXiv: https://arxiv.org/abs/1709.02228 · HTML: https://mlsanity.com/paper/1709.02228/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 30 of 36)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [SynCoLFinGer: Synthetic Contactless Fingerprint Generator](https://mlsanity.com/paper/2110.09144/) (2021)
- [Monocular 3D Fingerprint Reconstruction and Unwarping](https://mlsanity.com/paper/2205.00967/) (2022)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects](https://mlsanity.com/paper/2103.03038/) (2021)
- [Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06164/) (2022)
- [Fixed-length Dense Descriptor for Efficient Fingerprint Matching](https://mlsanity.com/paper/2311.18576/) (2023)
- [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)
- [Latent Fingerprint Matching via Dense Minutia Descriptor](https://mlsanity.com/paper/2405.01199/) (2024)
- [Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement](https://mlsanity.com/paper/2505.03597/) (2025)
- [Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection](https://mlsanity.com/paper/2208.10688/) (2022)
- [An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy](https://mlsanity.com/paper/2109.05526/) (2021)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [Deep Learning in the Field of Biometric Template Protection: An Overview](https://mlsanity.com/paper/2303.02715/) (2023)
- [MCLFIQ: Mobile Contactless Fingerprint Image Quality](https://mlsanity.com/paper/2304.14123/) (2023)
- [Synthetic Latent Fingerprint Generation Using Style Transfer](https://mlsanity.com/paper/2309.15734/) (2023)
- [Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning](https://mlsanity.com/paper/2402.10847/) (2024)
- [Improving Contactless Fingerprint Recognition with Robust 3D Feature Extraction and Graph Embedding](https://mlsanity.com/paper/2409.08782/) (2024)
- [FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis](https://mlsanity.com/paper/2512.18073/) (2025)
- [LEADER: Lightweight End-to-End Attention-Gated Dual Autoencoder for Robust Minutiae Extraction](https://mlsanity.com/paper/2602.15493/) (2026)
- [Unleashing the Power of Simplicity: A Minimalist Strategy for State-of-the-Art Fingerprint Enhancement](https://mlsanity.com/paper/2603.19004/) (2026)
- [Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement](https://mlsanity.com/paper/2605.25307/) (2026)
- [Comparative analysis of segmentation and generative models for fingerprint retrieval task](https://mlsanity.com/paper/2209.06172/) (2022)
- [A Universal Latent Fingerprint Enhancer Using Transformers](https://mlsanity.com/paper/2306.00231/) (2023)
- [Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition](https://mlsanity.com/paper/2403.16172/) (2024)
- [Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement](https://mlsanity.com/paper/2408.10823/) (2024)

## Abstract (arXiv)

Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet.