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Low cost gaze estimation: knowledge-based solutions.

Ion Martinikorena, Andoni Larumbe-Bergera, Mikel Ariz

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 22, 2019
    PubMed
    Summary

    This study presents new models for accurate eye tracking in low-resolution mobile devices. The proposed methods achieve high accuracy, even with user movement, advancing gaze estimation technology.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Eye tracking in low-resolution scenarios remains a significant challenge.
    • Mobile eye tracking offers potential for wider technology adoption in new fields.
    • Existing high-resolution gaze estimation paradigms inform new approaches.

    Purpose of the Study:

    • To develop and validate novel gaze estimation models for low-resolution remote systems.
    • To improve eye tracking accuracy by incorporating head pose estimation.
    • To address the limitations of current eye tracking technologies in mobile applications.

    Main Methods:

    • A knowledge-based approach for gaze estimation in low-resolution settings.
    • Development of three distinct models: geometrical, interpolation, and compound.
    • Integration of a head pose estimation method to enhance gaze accuracy.
    • Validation using the I2Head database, which includes head and gaze data.

    Main Results:

    • Experimental validation revealed model sensitivity to image processing inaccuracies.
    • The geometrical model showed particular sensitivity to image processing errors.
    • Compound and geometrical models demonstrated superior robustness during user displacement.
    • Achieved gaze estimation accuracy of approximately 3°, improving to nearly 5° in extreme displacement scenarios.

    Conclusions:

    • The proposed models offer viable solutions for gaze estimation in low-resolution remote systems.
    • Head pose estimation is crucial for improving overall gaze accuracy.
    • The developed methods are comparable to state-of-the-art techniques and show promise for mobile applications.