Deep Learning Era for Computer Vision-Based Eye Gaze Tracking: An Intensive Model

Abstract

The active theme for exploration is currently the potential for visual gaze tracking and visual human–machine interactions in contemporary user devices. Eye views were used as an input tool to extract human behavioral knowledge and to achieve immersive user interactions in augmented and amplified reality systems. However, implementations of gaze on consumer devices operating under controlled circumstances face tough precision and reliability challenges. For evolving automotive electronic systems such as driver surveillance systems and new touch screens, accurate and successful gaze estimates are significant. Such systems have to work efficiently and at minimum cost in demanding unregulated, low-power conditions. Deep learning is omnipresent for most computer vision tasks, but it is still common to estimate the eye gaze. The realistic account of all challenges and opportunities of eye gaze tracking requires a complete study of available deep learning methods. In this chapter, huge potential efforts are made to study the background of eye gaze tracking with the detail of how deep learning made a contribution to computer vision-based tracking. In the end, this chapter also highlights a generic system model and comparison of various algorithms for deep learning-driven eye gaze direction diagnosis.