Oleg Komogortsev, Ph.D - US grants

The need for accurate and unforgeable identity recognition techniques has become an issue of increasing urgency. Biometric approaches such as iris recognition hold huge promise but still have significant limitations, including susceptibility to 'spoofing'. This project seeks to advance our knowledge of security and accuracy of multibiometric systems by inventing, evaluating, and applying innovative methods and tools to combine highly accurate static traits, such as iris patterns, with novel traits based on the dynamics of eye movements. The strategy is to use existing iris recognition hardware to combine three different biometrics approaches related to the eye: measurement of iris patterns, unique characteristics of the eye globe and its muscles, and the brain's strategies for guiding visual attention. This multimodal ocular biometrics approach has the potential to improve liveness detection and resistance to sophisticated counterfeiting techniques and coercion attacks, while improving identification accuracy. This research tackles important questions related to the individuality, variability, scalability, and longevity of these ocular traits, building a foundation for security and accuracy improvement when those traits are combined with iris recognition. This project aims to benefit efforts such as the Unique Identification project in India, which seeks to use biometric information of 1.2 billion individuals to fight fraud.

Educational activities include three initiatives: 1) creation of a strong outreach activity to K-12 students, 2) expansion of an interdisciplinary research-oriented educational program previously created by the PI for undergraduate and graduate students, and 3) mentoring and guidance to interest undergraduate students in scientific careers and encourage more students from diverse backgrounds to pursue graduate study.

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.