Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques

Abstract

Pupil tracking plays a crucial role in various applications, including human–computer interactions, biometric identification, and Autostereoscopic three-dimensional (3D) displays, such as augmented reality (AR) 3D head-up displays (HUDs). This study aims to explore and compare advancements in pupil-tracking techniques using event camera imaging. Event cameras, also known as neuromorphic cameras, offer unique benefits, such as high temporal resolution and low latency, making them well-suited for capturing fast eye movements. For our research, we selected fast classical machine-learning-based computer vision techniques to develop our remote pupil tracking using event camera images. Our proposed pupil tracker combines local binary-pattern-features-based eye–nose detection with the supervised-descent-method-based eye-nose alignment. We evaluate the performance of event-camera-based techniques in comparison to traditional frame-based approaches to assess their accuracy, robustness, and potential for real-time applications. Consequently, our event-camera-based pupil-tracking method achieved a detection accuracy of 98.1% and a tracking accuracy (pupil precision < 10 mm) of 80.9%. The findings of this study contribute to the field of pupil tracking by providing insights into the strengths and limitations of event camera imaging for accurate and efficient eye tracking.