Eye-Tracking Feature Extraction for Biometric Machine Learning - Lim, Jia Zheng; Mountstephens, James; Teo, Jason (Frontiers in Neurorobotics, 2022)
Context: Eye-tracking is a technology to measure and determine an individual’s eye movements and eye positions. The eye data can be collected and recorded using an eye-tracker. Eye-tracking data offers unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this paper is to review what specific machine learning features can be obtained from eye-tracking data for the classification task. Methods: We performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used 4 independent electronic databases which were IEEE Xplore, ACM Digital Library, and ScienceDirect repositories as well as Google Scholar. The selection process was performed by using the PRISMA search strategy. We followed the processes indicated in PRISMA to choose the appropriate relevant papers. Results: Out of the initial 420 papers that were returned from our initial search query, 31 papers were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology. Conclusions: The features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, EOG, and gaze point. Fixation was the most commonly used feature among the studies found.