# FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

arXiv: https://arxiv.org/abs/2206.12885 · HTML: https://mlsanity.com/paper/2206.12885/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Semantic Image Inpainting with Deep Generative Models](https://mlsanity.com/paper/1607.07539/) (2016)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections](https://mlsanity.com/paper/1606.08921/) (2016)
- [Guided Image-to-Image Translation with Bi-Directional Feature Transformation](https://mlsanity.com/paper/1910.11328/) (2019)
- [End-to-End Latent Fingerprint Search](https://mlsanity.com/paper/1812.10213/) (2018)
- [Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge](https://mlsanity.com/paper/1712.09401/) (2017)
- [ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1808.00035/) (2018)
- [Generative Convolutional Networks for Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1705.01707/) (2017)

## Built on by (most cited first, 11 of 11)

- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [A Latent Fingerprint in the Wild Database](https://mlsanity.com/paper/2304.00979/) (2023)
- [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)
- [Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques](https://mlsanity.com/paper/2403.13916/) (2024)
- [Synthetic Latent Fingerprint Generation Using Style Transfer](https://mlsanity.com/paper/2309.15734/) (2023)
- [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)
- [Latent fingerprint enhancement for accurate minutiae detection](https://mlsanity.com/paper/2409.11802/) (2024)
- [A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae](https://mlsanity.com/paper/2504.15105/) (2025)
- [Intra-finger Variability of Diffusion-based Latent Fingerprint Generation](https://mlsanity.com/paper/2604.10040/) (2026)

## Abstract (arXiv)

Latent fingerprint enhancement is an essential pre-processing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates the latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network as FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground-truth instance in terms of the fingerprint skeleton map weighted by minutia locations and the orientation field regularized by the FOMFE model. Because minutia is the primary feature for fingerprint recognition and minutia can be retrieved directly from the fingerprint skeleton map, we offer a holistic framework which can perform latent fingerprint enhancement in the context of directly optimizing minutia information. This will help improve latent fingerprint identification performance significantly. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly. The codes will be available for non-commercial purposes from urlhttps://github.com/HubYZ/LatentEnhancement.