Year: 2020
Type: preprint
Abstract: The usage of eye tracking sensors is expected to grow in virtual (VR) and augmented reality (AR) platforms. Provided that users of these platforms consent to employing captured eye movement signals for authentication and health assessment, it becomes important to estimate oculomotor plant and brain function characteristics in real time. This paper shows a path toward that goal by presenting a parallel processing architecture capable of estimating oculomotor plant characteristics and comparing its performance to a single-threaded implementation. Results show that the parallel implementation improves the speed, accuracy, and throughput of oculomotor plant characteristic estimation versus the original serial version for both large-scale and real-time simulation.
Source: arXiv (Cornell University)
Institution: Texas State University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: ACHIEVING FINGERPRINT LEVEL PERFORMANCE WITH EYE MOVEMENT-BASED BIOMETRICS, CAREER: Secure and Trustworthy Ocular Biometrics, G3298843008, STANDARDIZATION AND QUALITY ASSESSMENT METHODS FOR THE ENHANCEMENT OF THE EYE MOVEMENT BIOMETRICS, SaTC: CORE: Small: Eye Movement Biometrics in Virtual and Augmented Reality, Graduate Reserach Fellowship Program (GRFP), G8179846106
OpenAlex ID: W3044325709