Summary of Enhancing Fingerprint Image Synthesis with Gans, Diffusion Models, and Style Transfer Techniques, by W. Tang et al.
Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques
by W. Tang, D. Figueroa, D. Liu, K. Johnsson, A. Sopasakis
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes innovative methods combining generative adversarial networks (GANs) and diffusion models to create high-quality, real and fake fingerprint images while preserving features like uniqueness and diversity. The authors demonstrate various techniques for generating live fingerprints from noise and translate these images to spoofed ones using image translation methods. To generate diverse types of spoof images with limited training data, the researchers employ style transfer techniques through a cycle autoencoder equipped with a Wasserstein metric and Gradient Penalty (CycleWGAN-GP) to prevent mode collapse and instability. The study finds that when spoof training data includes distinct characteristics, it leads to improved live-to-spoof translation. The authors evaluate the diversity and realism of generated images using the Fréchet Inception Distance (FID) and False Acceptance Rate (FAR). Their best diffusion model achieved a FID of 15.78, while the comparable WGAN-GP model performed better in uniqueness assessments with a lower FAR against training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to create very realistic fingerprint images that are hard to tell apart from real ones. The researchers used special computer programs called generative adversarial networks and diffusion models to make these fake fingerprints look like the real thing. They tested their methods by making fake fingerprints with different characteristics, like ridges and whorls, and showed that they can create realistic fingerprint images that are hard to distinguish from real ones. |
Keywords
» Artificial intelligence » Autoencoder » Diffusion » Diffusion model » Style transfer » Translation
