A Robust Webcam-based Eye Gaze Estimation System for Human-Computer Interaction

Abstract

Precise eye gaze detection has a multitude of real-life use cases such as the input mechanism for physically disabled persons, driver’s attention detection in vehicles, cheating detection in online exam, augmented reality, medical research and so on. Most of the applications need to support real-time functionality, thus the need for a fast and reliable method for eye gaze detection can be justified. In this research work, we propose a non-wearable and webcam-based eye-gaze detection method that offers multiple benefits in terms of accuracy, robustness, and reliability over existing solutions. We approached gaze detection as a multiclass classification problem, this reduced the complexity of the method and allows the solution to be implemented in a way that allows free head movement of the user. We leveraged the latest innovation and breakthroughs in deep learning to construct a novel eye-gaze detection method that works using the live video feed from any modern webcam with acceptable frame rates for proper real-time applications. We achieved 99% validation accuracy in gaze prediction and 20 FPS on average in real-time applications such as mouse pointer control and scrolling.