# Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

arXiv: https://arxiv.org/abs/1712.09401 · HTML: https://mlsanity.com/paper/1712.09401/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [Considerations on the Evaluation of Biometric Quality Assessment Algorithms](https://mlsanity.com/paper/2303.13294/) (2023)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [A Latent Fingerprint in the Wild Database](https://mlsanity.com/paper/2304.00979/) (2023)
- [MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence](https://mlsanity.com/paper/2106.05601/) (2021)
- [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)
- [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)
- [Pair-Relationship Modeling for Latent Fingerprint Recognition](https://mlsanity.com/paper/2207.00587/) (2022)
- [UFQA: Utility guided Fingerphoto Quality Assessment](https://mlsanity.com/paper/2407.11141/) (2024)
- [Latent fingerprint enhancement for accurate minutiae detection](https://mlsanity.com/paper/2409.11802/) (2024)
- [LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification](https://mlsanity.com/paper/2511.08119/) (2025)
- [AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction](https://mlsanity.com/paper/2609.07439/) (2026)

## Abstract (arXiv)

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet