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Implicit Calibration Using Probable Fixation Targets.

Pawel Kasprowski1, Katarzyna Harezlak2, Przemysław Skurowski3

  • 1Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. pawel.kasprowski@polsl.pl.

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Implicit eye tracker calibration uses stimulus information to predict gaze targets, aiming to replace time-consuming traditional methods. While currently less accurate, this intelligent approach shows potential for future applications.

Keywords:
calibrationeye movementeye trackingoptimization

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Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Computer Vision

Background:

  • Eye trackers are promising user-input devices, but their popularization is hindered by calibration challenges.
  • Traditional eye tracker calibration is time-consuming and requires unnatural user behavior.
  • Intelligent, implicit calibration methods are needed to overcome these limitations.

Purpose of the Study:

  • To introduce and evaluate a novel implicit eye tracker calibration method.
  • To explore algorithms for predicting probable fixation targets (PFTs) and gaze paths.
  • To assess the feasibility of implicit calibration for eye tracking applications.

Main Methods:

  • The proposed method identifies probable fixation targets (PFTs) based on visual stimuli.
  • It then constructs a probable gaze path using these PFTs for calibration.
  • Various algorithms for PFT detection and mapping were investigated and compared.

Main Results:

  • Implicit calibration, while currently yielding lower accuracy than classic methods, demonstrates potential.
  • Error analysis was performed using datasets from two distinct eye tracker types.
  • The results suggest implicit calibration may become sufficient for specific applications.

Conclusions:

  • Implicit eye tracker calibration offers a user-cooperation-free alternative to traditional methods.
  • Further development could lead to implicit calibration achieving comparable accuracy to classic techniques.
  • This approach paves the way for more seamless integration of eye trackers as user input devices.