用GAN生成逼真指纹图，解决数据少且隐私风险问题
Generation of Synthetic Fingerphotos with GANs
- 用StyleGAN2-ADA和StyleGAN3生成合成指纹图像
- 合成图像在特征统计上接近真实数据，跨图像匹配率高
- 适合研究生物识别数据生成与隐私保护的开发者
无接触指纹识别是一种新兴的生物认证方式，允许用户无需触碰扫描仪即可完成扫描。由于无接触指纹数据有限，且共享真实指纹存在安全风险，因此探索生成合成数据以替代或补充真实数据具有重要意义。本文提出并评估了使用StyleGAN2-ADA和StyleGAN3生成的合成指纹图像，通过对比其生物特征统计、真实与合成图像间的匹配得分，以及不同合成图像之间的匹配得分，来评估合成图像的真实性、隐私保护能力与多样性。本研究为未来合成指纹图像的评估提供了量化基准。评估代码已公开于https://github.com/cmillerlynch/fingerphoto-gan。
原文摘要 · Abstract (English)
Contactless fingerprinting is an emerging approach to biometric authentication that allows users to scan their fingerprints without touching a scanner. Due to the limited amount of contactless fingerprint data available and the security risks associated with sharing real individuals' fingerprints, it is valuable to explore methods of generating synthetic data that can be used in place of - or in conjunction with - real data to develop and evaluate contactless fingerprinting systems. In this paper, we present and evaluate synthetic fingerphotos generated using StyleGAN2-ADA and StyleGAN3, existing image generation architectures. We evaluate the realism, privacy preservation, and variety of the synthetic fingerphotos by comparing their biometric feature statistics to those of real fingerphotos, computing match scores between real and synthetic fingerphotos, and computing match scores between different synthetic fingerphotos. This paper provides a quantitative comparison point for future evaluations of synthetic fingerphotos. The evaluation code is made available at https://github.com/cmillerlynch/fingerphoto-gan.
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