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. Revolutionary developments of deep learning have captured significant attention in gaze estimation literature. Gaze estimation techniques have progressed from single-user constrained environments to multi-user 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. State-of-the-art approaches are analyzed based on deep learning model architectures, coordinate systems, environmental constraints, datasets and performance evaluation metrics. A key 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.