As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, liveness based attack detection methods provide a potential deterrent. We present a novel optokinetc nystagmus (OKN) based liveness assessment system for mobile applications which leverages phase-locked temporal features of a unique reflexive behavioral response. In this paper we provide proof of concept for eliciting, collecting and extracting the OKN response motion signature on a mobile device. Results of our most successful experimental machine learning classifier are reported for a multi-layer LSTM based model demonstrating a 98.4% single stimulus detection performance for simulated video based attacks.

作为对量化唯一性的实际追求，生物识别技术探索了使我们成为我们的参数，并提供了确保该身份完整性所需的工具。在我们不断连接的文化中，对生物识别安全的移动设备的日益依赖将其转变为不良行为者的目标。尽管没有任何系统能够阻止所有形式的入侵，但即使是最先进的生物识别方法也仍然容易受到欺骗攻击。随着这些攻击变得越来越复杂，基于活动的攻击检测方法提供了潜在的威慑力。我们为移动应用程序提出了一种新颖的基于视动眼震颤（OKN）的活力评估系统，该系统利用了独特反身行为反应的锁相时间特征。在本文中，我们提供了引发概念的证明，在移动设备上收集和提取OKN响应运动签名。我们针对基于LSTM的多层模型报告了最成功的实验机器学习分类器的结果，该模型证明了基于模拟视频的攻击具有98.4％的单刺激检测性能。