Eye Gaze Tracking for Real-World Systems: A Systematic Review

Eye gaze tracking has become a key feature for human-centered intelligent systems, and has wide variety of applications such as human–computer interaction, extended reality, behavioral analysis and safety-critical monitoring. Despite the rapid progress made in the field by deep learning and large-scale datasets, the field is still fragmented as reported performance often does not translate into robust real-world deployment. This paper presents a systematic review of eye gaze tracking research with regard to methodological development, sensing modalities, application areas, evaluation practice and recurring limitations. Following PRISMA guidelines, the contemporary literature involving learning paradigm and deep learning models are analyzed. This paper uses the evidence from cross-study evidence to explore the effect of dataset characteristics, evaluation protocols, and sensing choices on the reported performance. Particular attention is given to bias in datasets, fragility in evaluation, failure in generalization and the gap between application aspiration and the reality of deployment. The reviewed article continues with analysis of trade-offs among RGB, infrared, depth, and physiological modalities in order to demonstrate unrealized potential and existing shortcomings of multimodal fusion. The analysis reflects that future developments in the field of gaze tracking are less correlated to the incremental refinement of architecture, and more correlated to the importance of robustness-aware evaluations of benchmarks, the longitudinal evaluation and deployment of the algorithm, and the evaluations on the model. This review is intended to provide a comprehensive understanding of the current state of the art of eye gaze tracking that can be used to guide future research towards reliable real-world systems.