핵심 개념
Novel approaches using GANs and diffusion models for high-quality fingerprint image synthesis.
초록
The research explores the use of generative adversarial networks (GANs) and diffusion models to synthesize live and spoof fingerprint images while maintaining uniqueness. Various techniques, including image translation and style transfer, are employed to enhance the quality of generated images. Evaluation metrics such as Fréchet Inception Distance (FID) and False Acceptance Rate (FAR) are used to assess the realism and diversity of the generated fingerprints. The study also delves into the challenges of data scarcity in generating spoof fingerprints and proposes a method for artificial synthesis based on limited datasets.
Abstract:
- Introduction to biometric technologies and challenges in collecting fingerprint data.
- Distinction between live fingerprints and spoof fingerprints in biometric security.
- Introduction of Denoising Diffusion Probabilistic Model (DDPM) for artificial synthesis of fingerprint patches.
- Implementation of Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for comparison.
- Use of style transfer techniques like cycleGAN for blending global structures with local features in fingerprint images.
Methods:
- Description of DDPM architecture with U-Net structure.
- Comparison between WGAN-GP model and DDPM-v2 model in terms of FID, KID, Precision, Recall, Density, Coverage.
Results:
- Evaluation results show that DDPM-v2 outperforms other models in generating high-quality fingerprints.
- Analysis of FAR for assessing uniqueness between real impostors and synthetic pairs.
Conclusion:
- Proposal for synthesizing high-quality patch size fingerprint images using GANs and diffusion models while preserving unique features.
- Discussion on the success of fingerprint transformation techniques based on spoofiness levels.
통계
We find that when the spoof training data includes distinct spoof characteristics, it leads to improved live-to-spoof translation.
Our best diffusion model achieved a FID of 15.78.
The comparable WGAN-GP model achieved slightly higher FID while performing better in the uniqueness assessment due to a slightly lower FAR when matched against the training data, indicating better creativity.
Moreover, we give example images showing that a DDPM model clearly can generate realistic fingerprint images.