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Summary

This study presents an eye-tracking interface, evaluating pupil detection algorithms for real-time applications. The circular Hough transform achieved over 91% accuracy, enabling an eye-typing application with speeds exceeding 20 characters per minute.

Keywords:
detection rateeye trackinghuman computer interactionimage processingopen source softwarepupil detection algorithms

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Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Biomedical Engineering

Background:

  • Developing effective human-computer interfaces (HCIs) is crucial for real-time applications.
  • Eye-tracking technology offers a promising alternative input method.
  • Accurate pupil detection is fundamental for reliable eye-tracking performance.

Purpose of the Study:

  • To develop and evaluate an eye-tracking-based HCI for real-time applications.
  • To identify the optimal pupil detection algorithm for the proposed interface.
  • To assess the performance of novel and existing pupil detection techniques.

Main Methods:

  • Analyzed eight pupil detection algorithms, including six newly developed ones.
  • Evaluated algorithm accuracy using diverse eye image databases and a novel video testing protocol.
  • Measured detection rate, cursor controllability, stability, and running time across 30 subjects.

Main Results:

  • All developed algorithms achieved over 84% detection rate within a 50-pixel target.
  • The circular Hough transform approach yielded the highest accuracy at 91.39%.
  • The implemented interface facilitated an eye-typing application with a mean speed exceeding 20 characters per minute.

Conclusions:

  • The circular Hough transform is a highly effective pupil detection method for eye-tracking HCIs.
  • The developed eye-tracking interface demonstrates practical utility for real-time applications like eye typing.
  • Further research can optimize algorithms for even greater accuracy and speed in eye-tracking systems.