1 research outputs found
MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems
Analyzing the gaze accuracy characteristics of an eye tracker is a critical
task as its gaze data is frequently affected by non-ideal operating conditions
in various consumer eye tracking applications. In this study, gaze error
patterns produced by a commercial eye tracking device were studied with the
help of machine learning algorithms, such as classifiers and regression models.
Gaze data were collected from a group of participants under multiple conditions
that commonly affect eye trackers operating on desktop and handheld platforms.
These conditions (referred here as error sources) include user distance, head
pose, and eye-tracker pose variations, and the collected gaze data were used to
train the classifier and regression models. It was seen that while the impact
of the different error sources on gaze data characteristics were nearly
impossible to distinguish by visual inspection or from data statistics, machine
learning models were successful in identifying the impact of the different
error sources and predicting the variability in gaze error levels due to these
conditions. The objective of this study was to investigate the efficacy of
machine learning methods towards the detection and prediction of gaze error
patterns, which would enable an in-depth understanding of the data quality and
reliability of eye trackers under unconstrained operating conditions. Coding
resources for all the machine learning methods adopted in this study were
included in an open repository named MLGaze to allow researchers to replicate
the principles presented here using data from their own eye trackers.Comment: https://github.com/anuradhakar49/MLGaz