论文标题
CNN高参数调整应用于虹膜活性检测
CNN Hyperparameter tuning applied to Iris Liveness Detection
论文作者
论文摘要
由于其高稳定性和独特性，虹膜模式已显着改善了生物识别识别场。这种物理特征在安全性和其他相关领域中发挥了重要作用。但是，演示攻击（也称为欺骗技术）可用于用诸如印刷图像，人造眼和纹理隐形眼镜等文物的生物识别系统绕过生物识别系统。为了提高这些系统的安全性，已经提出了许多LIVISE检测方法，并于2013年启动了首次在国际虹膜LIVESITION检测竞赛中，以评估其有效性。在本文中，我们提出了对CASIA算法的高参数调整，中国科学院在2017年提交了第三次Iris Livices检测竞赛。拟议的修改促进了整体改进，并进行了8.48％的攻击呈现分类错误率（APCER）和0.18％BONAFIDE表现分类（BONAFIDE CHASTIONS CARTINATION CLENASINATION）的组合（bpcer）。评估了其他阈值值，以减少评估数据集上的APCER和BPCER之间的权衡并成功进行。
The iris pattern has significantly improved the biometric recognition field due to its high level of stability and uniqueness. Such physical feature has played an important role in security and other related areas. However, presentation attacks, also known as spoofing techniques, can be used to bypass the biometric system with artifacts such as printed images, artificial eyes, and textured contact lenses. To improve the security of these systems, many liveness detection methods have been proposed, and the first Internacional Iris Liveness Detection competition was launched in 2013 to evaluate their effectiveness. In this paper, we propose a hyperparameter tuning of the CASIA algorithm, submitted by the Chinese Academy of Sciences to the third competition of Iris Liveness Detection, in 2017. The modifications proposed promoted an overall improvement, with an 8.48% Attack Presentation Classification Error Rate (APCER) and 0.18% Bonafide Presentation Classification Error Rate (BPCER) for the evaluation of the combined datasets. Other threshold values were evaluated in an attempt to reduce the trade-off between the APCER and the BPCER on the evaluated datasets and worked out successfully.
