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Iris Liveness Detection by Relative Distance Comparisons

机译：相对距离比较的虹膜活力检测

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The focus of this paper is on presentation attack detection for the iris biometrics, which measures the pattern within the colored concentric circle of the subjects' eyes, to authenticate an individual to a generic user verification system. Unlike previous deep learning methods that use single convolutional neural network architectures, this paper develops a framework built upon triplet convolutional networks that takes as input two real iris patches and a fake patch or two fake patches and a genuine patch. The aim is to increase the number of training samples and to generate a representation that separates the real from the fake iris patches. The smaller architecture provides a way to do early stopping based on the liveness of single patches rather than the whole image. The matching is performed by computing the distance with respect to a reference set of real and fake examples. The proposed approach allows for real-time processing using a smaller network and provides equal or better than state-of-the-art performance on three benchmark datasets of photo-based and contact lens presentation attacks.
机译：本文的重点是演示攻击检测虹膜生物识别，测量受试者的眼睛的颜色的同心圆内的模式，以个人身份到一个通用的用户验证系统。与使用单一的卷积神经网络架构之前的深度学习方法，本文开发了在三线态卷积网络，作为输入两个实虹膜补丁和一个假的补丁或两个假补丁和补丁真正建立了一个框架。其目的是提高训练样本的数目，并生成用于分隔假虹膜补丁实际的表示。较小的架构提供了一种方法做基于单补丁而不是整个图像的活跃度早期停止。匹配通过相对于参考集的真假实例计算距离进行。所提出的方法允许使用较小的网络实时处理，并提供等于或大于上的照片为基础的和隐形眼镜呈现攻击3个基准数据集的状态的最先进的性能更好。

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