论文标题
虹膜识别系统的演示攻击检测中的人口偏见
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
论文作者
论文摘要
随着生物识别系统的广泛使用，人口偏见问题引起了更多关注。尽管许多研究解决了生物识别验证中的偏见问题，但尚无作品来分析呈现攻击检测（PAD）决策的偏见。因此，我们在本文中调查和分析了虹膜垫算法中的人口偏见。为了进行明确的讨论，我们将差异性能和差异结果的概念调整为PAD问题。我们使用NDCLD-2013数据库使用三个基线（手工制作，转移学习和从头开始的手工制作，学习和训练）研究虹膜垫中的偏差。实验结果指出，与男性相比，女性用户受PAD的保护大大降低。
With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD-2013 database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.
