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<p>The existing webcam-based eye-tracking methods are often inaccurate when applied to large-screen unmanned automatic kiosks, due to significant camera-to-user distances, head pose variability, and time-consuming calibration procedures, limiting their suitability for public kiosk applications. This research aims to develop and evaluate a novel, accurate webcam-based eye-tracking system specifically designed for interaction with large-screen kiosks, overcoming the challenge of camera distance. We propose a spatial attention–based deep learning feature extractor to obtain high-fidelity 3D facial mesh and iris landmarks under varied head poses and distances, coupled with lightweight machine-learning regression models for screen coordinate prediction. In addition, we proposed a novel smooth-moving calibration scheme with adjustable speed to reduce calibration time and improve user engagement. The system was tested on a custom-built kiosk featuring a 32-inch display (1080 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> 1920 pixels). Experiments compare four regression models, including Linear Regression with Stochastic Gradient Descent (SGD) and Ridge Regression, each tested with four distinct user calibration methods. The smooth-moving calibration point proved to be the most effective calibration method. The SGD model achieved the highest accuracy at 96%, with an average pixel error of 73.42px on the x-axis and 140.99px on the y-axis. The Ridge Regression model also performed well, obtaining 84% accuracy. Notably, when the calibration time for the Ridge Regression model was reduced from 80 to 40 seconds, its average pixel error improved significantly to 48px on the x-axis and 87px on the y-axis. This study demonstrates the successful implementation of a high-accuracy eye-tracking system for large-screen kiosks using only a standard webcam. The proposed method, particularly the combination of Ridge Regression with a shortened calibration time, presents a robust and efficient solution that is practical for real-world deployment in interactive public systems.</p>

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Lightweight webcam-based eye tracking system for large display screens

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Ivan Fenyom,
  • Adeyinka Adedigba,
  • Daison Darlan,
  • Oladayo S. Ajani,
  • Rammohan Mallipeddi,
  • Hwang Jae Joon

摘要

The existing webcam-based eye-tracking methods are often inaccurate when applied to large-screen unmanned automatic kiosks, due to significant camera-to-user distances, head pose variability, and time-consuming calibration procedures, limiting their suitability for public kiosk applications. This research aims to develop and evaluate a novel, accurate webcam-based eye-tracking system specifically designed for interaction with large-screen kiosks, overcoming the challenge of camera distance. We propose a spatial attention–based deep learning feature extractor to obtain high-fidelity 3D facial mesh and iris landmarks under varied head poses and distances, coupled with lightweight machine-learning regression models for screen coordinate prediction. In addition, we proposed a novel smooth-moving calibration scheme with adjustable speed to reduce calibration time and improve user engagement. The system was tested on a custom-built kiosk featuring a 32-inch display (1080 \(\times\) 1920 pixels). Experiments compare four regression models, including Linear Regression with Stochastic Gradient Descent (SGD) and Ridge Regression, each tested with four distinct user calibration methods. The smooth-moving calibration point proved to be the most effective calibration method. The SGD model achieved the highest accuracy at 96%, with an average pixel error of 73.42px on the x-axis and 140.99px on the y-axis. The Ridge Regression model also performed well, obtaining 84% accuracy. Notably, when the calibration time for the Ridge Regression model was reduced from 80 to 40 seconds, its average pixel error improved significantly to 48px on the x-axis and 87px on the y-axis. This study demonstrates the successful implementation of a high-accuracy eye-tracking system for large-screen kiosks using only a standard webcam. The proposed method, particularly the combination of Ridge Regression with a shortened calibration time, presents a robust and efficient solution that is practical for real-world deployment in interactive public systems.