# A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae

arXiv: https://arxiv.org/abs/2504.15105 · HTML: https://mlsanity.com/paper/2504.15105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [End-to-End Latent Fingerprint Search](https://mlsanity.com/paper/1812.10213/) (2018)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)

## Abstract (arXiv)

Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancement strategies, we propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies. Furthermore, to improve the generalization capability of the network, we integrate orientation field and minutiae-related modules into TBSFNet and introduce a Multi-Level Feature Guidance Network (MLFGNet). Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms.