Abstract
Eye tracking has been a topic of interest in research in recent years because it provides convenience to a wide range of applications. It is acknowledged as an important non-traditional method of human–computer interaction. Eye tracking is a useful tool for determining where and when people devote visual attention to a scene, and it helps to understand cognitive functioning. Nowadays, eye-tracking technology is making its way from the lab to the real world, collecting more data at a faster rate and with a greater variety of data kinds. Eye tracking will become closer to big data if the current trend continues. A real-time model is created using machine learning methodology, which tests a high-accuracy hypothesis. Eye tracking with parameters looks into a participant’s eye movements while presenting them with a variety of options. Using machine learning to analyze eye movements and extract attributes to assess eye behavior. K-nearest neighbor, Naive Bayes, decision trees, and random forests are machine learning algorithms that produce models with improved accuracy. In this paper, we have reviewed different eye-tracking technologies to obtain eye movement parameters and classifiers for categorization, such as machine learning and deep learning toward recognition of cognitive processes involved in learning.
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Saini, S., Roy, A.K., Basu, S. (2023). Eye-Tracking Movements—A Comparative Study. In: Bhattacharyya, S., Das, G., De, S., Mrsic, L. (eds) Recent Trends in Intelligence Enabled Research. DoSIER 2022. Advances in Intelligent Systems and Computing, vol 1446. Springer, Singapore. https://doi.org/10.1007/978-981-99-1472-2_3
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DOI: https://doi.org/10.1007/978-981-99-1472-2_3
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