# Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2201.06164 · HTML: https://mlsanity.com/paper/2201.06164/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation](https://mlsanity.com/paper/1908.00999/) (2019)
- [Time-Travel Rephotography](https://mlsanity.com/paper/2012.12261/) (2020)
- [Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN](https://mlsanity.com/paper/1812.10482/) (2018)
- [High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy](https://mlsanity.com/paper/2105.10403/) (2021)
- [Fingerprint Synthesis: Search with 100 Million Prints](https://mlsanity.com/paper/1912.07195/) (2019)
- [SynFi: Automatic Synthetic Fingerprint Generation](https://mlsanity.com/paper/2002.08900/) (2020)
- [Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints](https://mlsanity.com/paper/1304.5409/) (2013)

## Built on by (most cited first, 7 of 7)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2405.04538/) (2024)
- [Fingerprint Membership and Identity Inference Against Generative Adversarial Networks](https://mlsanity.com/paper/2406.15253/) (2024)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [Ridgeformer: Mutli-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition](https://mlsanity.com/paper/2506.01806/) (2025)
- [Conditional Synthetic Live and Spoof Fingerprint Generation](https://mlsanity.com/paper/2510.17035/) (2025)

## Abstract (arXiv)

Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple different fingerprint images per finger. In particular, we introduce the SynFing synthetic fingerprints dataset consisting of 100K image pairs, each pair corresponding to the same identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction. It significantly improved the realism of the generated fingerprints, both visually and in terms of their ability to spoof fingerprint-based verification systems. The code and fingerprints dataset are publicly available: https://github.com/rafaelbou/fingerprint_generator.