  Eye gaze analysis is an important research problem in the field of Computer
Vision and Human-Computer Interaction. Even with notable progress in the last
10 years, automatic gaze analysis still remains challenging due to the
uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and
illumination conditions. There are several open questions, including what are
the important cues to interpret gaze direction in an unconstrained environment
without prior knowledge and how to encode them in real-time. We review the
progress across a range of gaze analysis tasks and applications to elucidate
these fundamental questions, identify effective methods in gaze analysis, and
provide possible future directions. We analyze recent gaze estimation and
segmentation methods, especially in the unsupervised and weakly supervised
domain, based on their advantages and reported evaluation metrics. Our analysis
shows that the development of a robust and generic gaze analysis method still
needs to address real-world challenges such as unconstrained setup and learning
with less supervision. We conclude by discussing future research directions for
designing a real-world gaze analysis system that can propagate to other domains
including Computer Vision, Augmented Reality (AR), Virtual Reality (VR), and
Human Computer Interaction (HCI). Project Page:
https://github.com/i-am-shreya/EyeGazeSurvey}{https://github.com/i-am-shreya/EyeGazeSurvey
