Eye gaze estimation: A survey on deep learning-based approaches

Abstract

Human gaze estimation plays a major role in many applications in human-computer interaction and computer vision by identifying the users’ point-of-interest. The revolutionary developments of deep learning have captured significant attention in the gaze estimation literature. Gaze estimation techniques have progressed from single-user constrained environments to multiuser unconstrained environments with the applicability of deep learning techniques in complex unconstrained environments with extensive variations. This paper presents a comprehensive survey of the single-user and multi-user gaze estimation approaches with deep learning. The state-of-the-art approaches are analyzed based on deep learning model architectures, coordinate systems, environmental constraints, datasets and performance evaluation metrics.Akey outcome from this survey realizes the limitations, challenges, and future directions of multi-user gaze estimation techniques. Furthermore, this paper serves as a reference point and a guideline for future multi-user gaze estimation research.