mlsanityData to 14 Oct 2026

High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy

arXiv 2105.10403 ↗First posted 21 May 2021cs.CV eess.IVcited by 14

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Abstract (arXiv)

In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, 512×512 pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/ClarksonFingerGen

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

8 of 21 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  5. 2017Improved Training of Wasserstein GANscited by 2,235
  6. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  7. 2018Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GANcited by 16
  8. 2013Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprintscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 3; 2025: 2; 2026: 2.

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  1. 2022PrintsGAN: Synthetic Fingerprint Generatorcited by 30
  2. 2022SpoofGAN: Synthetic Fingerprint Spoof Imagescited by 17
  3. 2022Synthetic Latent Fingerprint Generatorcited by 11
  4. 2024Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditionscited by 10
  5. 2022Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networkscited by 7
  6. 2023RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognitioncited by 7
  7. 2023FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Datacited by 6
  8. 2025Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Modelscited by 3
  9. 2024Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniquescited by 2
  10. 2023Synthetic Latent Fingerprint Generation Using Style Transfercited by 1
  11. 2025Conditional Synthetic Live and Spoof Fingerprint Generationcited by 1
  12. 2024Unbalanced Fingerprint Classification for Hybrid Fingerprint Orientation Mapscited by 0
2 more
  1. 2026Intra-finger Variability of Diffusion-based Latent Fingerprint Generationcited by 0
  2. 2026Identity-Consistent Multi-Pose Generation of Contactless Fingerprintscited by 0