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合成虹膜图像:卡特西安和极地表示之间的比较分析
Adrian Kordas1, Ewelina Bartuzi-Trokielewicz1, Michał Ołowski1
1Department of Biometrics, NASK-National Research Institute, 01-045 Warsaw, Poland.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
生成对抗网络 (GAN) 可以为生物识别系统创建现实的合成虹膜图像. 这些生成的虹膜显示了足够的纹理来区分,增强了培训数据集,没有身份泄露.
科学领域:
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 生成技术,特别是生成对抗网络 (GANs),使合成生物识别数据的创建成为可能.
- 虹膜识别系统从多样化和大型数据集中受益,以提高准确性.
研究的目的:
- 使用StyleGAN3模型生成人造虹膜图像.
- 分析合成虹膜纹理密度和适合特征提取的适度.
- 在卡特西安和极地坐标表示中评估生成的虹彩.
主要方法:
- 使用StyleGAN3进行合成虹膜图像生成.
- 产生了1327个独特的合成虹膜样本.
- 使用OSIRIS和wordlcoin-openiris软件测试生成的虹膜,用于区分和身份泄露检测.
主要成果:
- 在生成的虹膜中没有观察到来自训练集的身份泄露.
- 生成的虹膜具有足够的独特纹理信息,可以成功分化.
- 合成虹膜成功地从彼此和真实虹膜样本中区分出来.
结论:
- 合成虹膜数据生成显示了增强培训数据集的巨大潜力.
- 生成的虹膜数据可以提高虹膜识别系统的性能,准确性和稳定性.
- 探索卡特西安和极点表示有助于理解合成数据对生物识别的好处.
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