Summary of Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions, by Steven A. Grosz and Anil K. Jain
Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions
by Steven A. Grosz, Anil K. Jain
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper presents GenPrint, a framework for generating synthetic fingerprints that can alleviate privacy concerns in biometric recognition. Unlike previous methods, GenPrint enables the creation of novel styles from unseen devices without requiring additional fine-tuning. The framework uses latent diffusion models with multimodal conditions (text and image) to generate fingerprint images with intra-class variations while maintaining identity. Experimental results demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images. The generated images yield comparable or even superior accuracy to models trained solely on real data and further enhance performance when augmenting the diversity of existing real fingerprint datasets. Keywords: synthetic fingerprints, GenPrint, latent diffusion models, multimodal conditions, biometric recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make fake fingerprints that are very realistic. It’s important because it can help keep people’s personal information private. The old ways of making fake fingerprints were limited and didn’t look like real fingers. This new method, called GenPrint, makes different types of fingerprints while keeping the same identity. It also lets you control how the fingerprints look. The researchers tested their method using pictures from the internet and found that it works really well. The fake fingerprints are so good that they can even help make real fingerprint recognition better. |
Keywords
» Artificial intelligence » Fine tuning
