SaTC: CORE: Small: Eye Movement Biometrics in Virtual and Augmented Reality

Texas State University - San Marcos, San Marcos TX

Investigators

Abstract

Virtual and augmented reality (VR/AR) applications are expected to play an increasingly important role in many aspects of everyday life; however, we do not yet have effective methods for protecting VR/AR systems from cybersecurity threats. The goal of this research is to make VR/AR systems more secure via the development of highly accurate and counterfeit-resistant biometric techniques based on eye movements. These techniques are based on the computational modeling of multiple characteristics of the way individuals move their eyes. The development of trustworthy solutions for performing biometric recognition in such systems is critical for the creation of a cybersecurity infrastructure that can adequately serve emerging applications of VR/AR for social networking, health monitoring, and economic transactions. Improved understanding of distinctive eye movement features could also facilitate their use for the detection of cyber-sickness, stress, fatigue, concussions and other states that manifest in abnormalities of human vision. The education component of the project will help recruit a greater number of diverse students to careers in computer science as well as interdisciplinary studies involving computer science, and it will better prepare students to be key players in the next generation of innovators. The goal of this project is to advance the current state of security in VR/AR systems via the development of highly accurate and counterfeit-resistant biometric techniques based on eye movements. The problem of eye movement-driven biometrics in VR/AR environments is significantly more challenging due to the 3-D environment which produces very complex eye movements that are hard to accurately classify and also the much larger number of extracted eye movement-driven features when compared to the eye movement-driven biometrics in 2D spaces. This project has two major thrusts: (1) biometric recognition: establishing the baseline for person recognition performance via eye movement characteristics in VR/AR environments; and (2) counterfeit-resistance: researching the robustness against spoofing attacks (e.g., attempts to defeat a biometric system through the introduction of fake biometric samples). This research provides answers to important questions related to the uniqueness, variability, scalability, and longevity of eye movement characteristics in VR/AR environments. The outcome of this work will be a new method to address the biometric security vulnerabilities of current and future VR/AR systems.