# Level Three Synthetic Fingerprint Generation

arXiv: https://arxiv.org/abs/2002.03809 · HTML: https://mlsanity.com/paper/2002.03809/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [PrintsGAN: Synthetic Fingerprint Generator](https://mlsanity.com/paper/2201.03674/) (2022)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [Synthetic Latent Fingerprint Generator](https://mlsanity.com/paper/2208.13811/) (2022)
- [Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions](https://mlsanity.com/paper/2404.13791/) (2024)
- [RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition](https://mlsanity.com/paper/2307.14016/) (2023)
- [FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data](https://mlsanity.com/paper/2310.19024/) (2023)
- [Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition](https://mlsanity.com/paper/2202.05297/) (2022)
- [Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques](https://mlsanity.com/paper/2403.13916/) (2024)
- [Conditional Synthetic Live and Spoof Fingerprint Generation](https://mlsanity.com/paper/2510.17035/) (2025)
- [Fingerprint Pore Detection: A Survey](https://mlsanity.com/paper/2211.14716/) (2022)
- [RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images](https://mlsanity.com/paper/2308.09285/) (2023)
- [Generation of Synthetic Fingerphotos with GANs](https://mlsanity.com/paper/2608.15029/) (2026)

## Abstract (arXiv)

Today's legal restrictions that protect the privacy of biometric data are hampering fingerprint recognition researches. For instance, all high-resolution fingerprint databases ceased to be publicly available. To address this problem, we present a novel hybrid approach to synthesize realistic, high-resolution fingerprints. First, we improved Anguli, a handcrafted fingerprint generator, to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a CycleGAN to transform these maps into realistic fingerprints. Unlike other CNN-based works, we can generate several images for the same identity. We used our approach to create a synthetic database with 7400 images in an attempt to propel further studies in this field without raising legal issues. We included sweat pore annotations in 740 images to encourage research developments in pore detection. In our experiments, we employed two fingerprint matching approaches to confirm that real and synthetic databases have similar performance. We conducted a human perception analysis where sixty volunteers could hardly differ between real and synthesized fingerprints. Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.