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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Adaptive Linear Regression for Appearance-Based Gaze Estimation.

Feng Lu, Yusuke Sugano, Takahiro Okabe

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
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    Summary
    This summary is machine-generated.

    This study introduces an adaptive linear regression (ALR) method to improve appearance-based gaze estimation. The ALR method significantly reduces training samples needed for accurate eye-tracking, even with head motion and blinking.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Appearance-based gaze estimation faces challenges with limited training data, head motion, varying image resolution, and eye blinks.
    • Accurate gaze tracking is crucial for applications ranging from assistive technologies to user behavior analysis.

    Purpose of the Study:

    • To develop a robust and efficient method for appearance-based gaze estimation.
    • To address the practical limitations of existing gaze estimation techniques, particularly the need for extensive training data.

    Main Methods:

    • Proposed an adaptive linear regression (ALR) method to map high-dimensional eye image features to low-dimensional gaze positions.
    • Utilized ℓ(1)-optimization within the ALR framework to select the sparsest, most optimal training samples.
    • Integrated gaze estimation, subpixel alignment, and blink detection into a unified optimization framework.

    Main Results:

    • Significantly reduced the number of required training samples for high-accuracy gaze estimation.
    • Successfully handled variations in head motion, image resolution, and eye blinking.
    • Demonstrated effectiveness through experiments with multiple users under diverse conditions.

    Conclusions:

    • The proposed adaptive linear regression method offers a more practical and accurate solution for appearance-based gaze estimation.
    • Simultaneous optimization of gaze estimation, alignment, and blink detection enhances robustness.
    • This approach paves the way for more reliable and less data-intensive eye-tracking systems.