# Latent fingerprint enhancement for accurate minutiae detection

arXiv: https://arxiv.org/abs/2409.11802 · HTML: https://mlsanity.com/paper/2409.11802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge](https://mlsanity.com/paper/1712.09401/) (2017)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1808.00035/) (2018)
- [Latent fingerprint minutia extraction using fully convolutional network](https://mlsanity.com/paper/1609.09850/) (2016)
- [Generative Convolutional Networks for Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1705.01707/) (2017)
- [Hardware Implementation of Multimodal Biometric using Fingerprint and Iris](https://mlsanity.com/paper/2201.05996/) (2022)
- [A Robust Algorithm for Contactless Fingerprint Enhancement and Matching](https://mlsanity.com/paper/2408.09426/) (2024)

## Abstract (arXiv)

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.