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Related Experiment Video

Updated: Aug 4, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

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Non-Intrusive Real Time Eye Tracking Using Facial Alignment for Assistive Technologies.

C Leblond-Menard, S Achiche

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 6, 2023
    PubMed
    Summary
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    This study introduces a lightweight, accurate eye-tracking system using convolutional neural networks and a webcam. It offers a faster, more accessible alternative for assistive technologies, improving gaze estimation on mobile devices.

    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Assistive Technology

    Background:

    • Traditional eye tracking systems are often intrusive (head-mounted cameras) or limited by environmental factors (infrared reflections).
    • Existing methods pose challenges for long-term use in assistive technologies and can be unreliable in varying light conditions.

    Purpose of the Study:

    • To develop an accurate and lightweight eye-tracking solution for assistive tasks.
    • To enable gaze estimation using a simple webcam and convolutional neural networks.

    Main Methods:

    • Utilized state-of-the-art convolutional neural network face alignment algorithms.
    • Employed a standard webcam for gaze, face position, and pose estimation.
    • Focused on appearance-based gaze estimation for improved accessibility.

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    Related Experiment Videos

    Last Updated: Aug 4, 2025

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.7K
    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
    06:49

    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

    Published on: January 10, 2014

    27.3K
    Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
    07:30

    Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

    Published on: March 21, 2019

    8.0K

    Main Results:

    • Achieved significantly faster computation times (up to 91% decrease) compared to current state-of-the-art methods.
    • Maintained comparable accuracy with average errors of 4.5° (MPIIGaze), 3.9° (UTMultiview), and 3.3° (GazeCapture).
    • Demonstrated the feasibility of accurate gaze estimation on mobile devices.

    Conclusions:

    • The proposed webcam-based eye-tracking system is a viable, efficient, and accurate solution for assistive technologies.
    • This approach overcomes the limitations of intrusive and environmentally sensitive traditional systems.
    • Enables broader application of gaze estimation, including on resource-constrained mobile platforms.