Eye-Tracking Feature Extraction for Biometric Machine Learning

Abstract

ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer 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 article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.MethodsWe 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 four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality

“…Most studies described in the previous section defined various classification models and their performances, highlighting the role and influence of eye-tracking data [ 34 , 35 ] on characterizing users and tasks in specific scenarios. Some of the most widely used eye-tracking features include pupil diameter, pupil position, eye fixations, blinking, and gaze point.…”

“…Various types of classification algorithms, including Support Vector Machine (SVM), k -Nearest Neighbors (kNN), Decision Trees, Logistic Regression, and Na ve Bayes have been employed to track this data. SVM appears to be the most promising [ 34 , 35 ]. Recent studies have begun to employ deep learning algorithms [ 18 , 36 ], such as CNN and LSTM, but the number of deep learning-based modeling research is smaller than that of machine learning-based one.…”

Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification

“…In general, eye-tracking information is computed and acquired by using an eye-tracking sensor/camera. The acquired data offer many features and are useful in various classification tasks ( Lim et al, 2022 ; Holmqvist et al, 2023 ). Eye tracking metrics are used for disclosing perceptions about the participant’s actions and mindset in different circumstances.…”

Quantifying dwell time with location-based augmented reality: Dynamic AOI analysis on mobile eye tracking data with vision transformer

“…(Carter & Luke, 2020). Despite being used in a growing range of contexts, many eye tracking measures are little known (Lim et al, 2022) and new methods to extract knowledge from eye tracking data are constantly emerging (Simpson, 2021), which reflects the technique's relative lack of maturity (Lappi, 2015). Eye tracking data is often of uneven quality, making it notoriously difficult to analyze (Ahlström et al, 2012).…”