基于生成对抗网络（GANs）的合成指纹照片生成

中文摘要 AI 辅助

非接触式指纹识别是一种新兴的生物特征认证方法，允许用户无需接触扫描仪即可扫描指纹。由于可用的非接触式指纹数据有限，且共享真实个体指纹存在安全风险，探索可替代真实数据或与真实数据结合使用的合成数据生成方法，以开发和评估非接触式指纹识别系统具有重要价值。本文提出并评估了使用现有图像生成架构StyleGAN2-ADA和StyleGAN3生成的合成指纹照片，通过将其生物特征统计数据与真实指纹照片对比、计算真实与合成指纹照片间的匹配分数、计算不同合成指纹照片间的匹配分数，来评估合成指纹照片的真实性、隐私保护性和多样性。本文为合成指纹照片的后续评估提供了定量对比基准，评估代码已在指定URL公开。

英文摘要

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.

发表机构

• Clarkson University（克拉克森大学）
• UNC Charlotte（北卡罗来纳大学夏洛特分校）

机构由 AI 辅助整理，请以论文原文为准。