# Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning

arXiv: https://arxiv.org/abs/2402.10847 · HTML: https://mlsanity.com/paper/2402.10847/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [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)
- [Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge](https://mlsanity.com/paper/1712.09401/) (2017)
- [Finger-UNet: A U-Net based Multi-Task Architecture for Deep Fingerprint Enhancement](https://mlsanity.com/paper/2310.00629/) (2023)

## Built on by (most cited first, 1 of 1)

- [FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis](https://mlsanity.com/paper/2512.18073/) (2025)

## Abstract (arXiv)

Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancement, our method employs a specialized encoder to derive representations from fingerprint images in a self-supervised manner. We further refine these representations, aiming to enhance the verification capabilities. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance against established self-supervised training techniques. Our findings not only highlight the effectiveness of our method but also pave the way for potential advancements. Crucially, our research indicates that it is feasible to extract meaningful fingerprint representations from degraded images without relying on enhanced samples.