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MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems.

Anuradha Kar1

  • 1École Normale Supérieure de Lyon, 46 Allée d'Italie, 69007 Lyon, ‎France.

Vision (Basel, Switzerland)
|May 13, 2020
PubMed
Summary

Machine learning models successfully identified and predicted gaze error patterns in eye trackers, improving data quality analysis under real-world conditions. This research enhances understanding of eye tracking reliability.

Keywords:
eye gazegaze datamachine learningmodellingneural networkspattern recognition

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Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Machine Learning

Background:

  • Eye tracker data quality is crucial for consumer applications but often degraded by non-ideal conditions.
  • Previous research focused on modeling human behavior, neglecting the identification and quantification of gaze error sources.
  • Understanding gaze error sources is vital for improving eye tracker data reliability.

Purpose of the Study:

  • To investigate the efficacy of machine learning (ML) for detecting and predicting gaze error patterns in eye trackers.
  • To quantify the impact of various error sources on eye tracker data quality.
  • To enable a deeper understanding of eye tracker reliability under unconstrained use.

Main Methods:

  • Collected gaze data from participants under conditions like varying user/eye-tracker pose and distance.
  • Employed ML algorithms, including classifiers and regression models, to analyze gaze error patterns.
  • Trained ML models using collected data to identify and predict gaze error levels.

Main Results:

  • ML models successfully identified impacts of different error sources, which were indistinguishable by visual inspection or basic statistics.
  • Models accurately predicted variability in gaze error levels caused by these conditions.
  • Demonstrated ML's capability to analyze eye tracking data quality beyond traditional methods.

Conclusions:

  • Machine learning offers a powerful approach to detect and predict gaze error patterns in eye trackers.
  • This method enhances the understanding of data quality and reliability in real-world eye tracking applications.
  • Open-source resources (MLGaze) are provided for reproducibility and further research.