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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Iris Geometric Transformation Guided Deep Appearance-Based Gaze Estimation.

Wei Nie, Zhiyong Wang, Weihong Ren

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 6, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Iris Geometric Transformation Guided Gaze estimation (IGTG-Gaze) to improve gaze estimation by explicitly modeling iris geometry. The new method enhances deep learning models for more accurate gaze direction prediction.

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    Area of Science:

    • Computer Vision
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Current deep learning models for gaze estimation often overlook explicit geometric relationships between iris appearance and gaze direction.
    • Leveraging iris features through latent feature sharing is common but can be suboptimal.

    Purpose of the Study:

    • To propose a novel module, Iris Geometric Transformation Guided Gaze estimation (IGTG-Gaze), that explicitly models geometric relationships for improved gaze estimation.
    • To integrate geometric assumptions about the eyeball's structure into deep representation learning for gaze direction.

    Main Methods:

    • Revisiting the physiological structure of the eyeball to establish geometric assumptions, such as the iris center's normal vector approximating gaze direction.
    • Developing the IGTG-Gaze module with an explicit geometric parameter sharing mechanism linking gaze direction and iris landmarks.
    • Integrating the module into various deep neural networks for end-to-end optimization.

    Main Results:

    • IGTG-Gaze seamlessly integrates into diverse deep neural networks.
    • The module demonstrates flexibility by extending from sparse iris landmarks to dense eye mesh.
    • Consistent leading performance is achieved in both within- and cross-dataset evaluations.

    Conclusions:

    • IGTG-Gaze offers a practical and effective approach for enhancing deep gaze representation from appearance.
    • Explicitly modeling geometric relationships significantly improves gaze estimation accuracy.
    • The method provides a robust and adaptable solution for appearance-based gaze estimation.