Optokinetic response for mobile device biometric liveness assessment

Abstract

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.