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    This study introduces a novel temple-mounted eye tracker for head-mounted displays and augmented reality glasses. The design ensures accurate gaze estimation with a slim form-factor, overcoming space limitations in wearable devices.

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    Area of Science:

    • Human-Computer Interaction
    • Wearable Technology
    • Computer Vision

    Background:

    • Eye trackers are vital for advanced display systems like head-mounted displays (HMDs) and augmented reality (AR) glasses.
    • Existing eye trackers face challenges in accuracy and robustness due to space constraints for components in compact devices.
    • Developing unobtrusive and accurate gaze estimation solutions is critical for seamless AR/VR integration.

    Purpose of the Study:

    • To propose a novel, slim eye tracker design integrated into foldable temples for HMDs and AR glasses.
    • To enable accurate gaze estimation while maintaining a compact form-factor and unobstructed user vision.
    • To present a new calibration and gaze extraction method optimized for the temple-mounted system.

    Main Methods:

    • Developed a temple-mounted eye tracker utilizing a near-infrared (NIR) imaging system.
    • Incorporated a patterned NIR mirror for calibration markers.
    • Introduced a unique calibration and gaze extraction algorithm considering the mirror's spatial reflectance properties.
    • Evaluated gaze extraction accuracy with multiple users in realistic scenarios.

    Main Results:

    • Demonstrated accurate gaze estimation capabilities with the novel temple-mounted eye tracker design.
    • Wearable prototypes were successfully developed and tested.
    • The proposed algorithm effectively utilized the patterned mirror for calibration and gaze extraction.
    • User studies confirmed the system's performance in realistic usage conditions.

    Conclusions:

    • The temple-mounted eye tracker offers a viable solution for accurate gaze estimation in space-constrained wearable devices.
    • The design facilitates slim form-factors and unobstructed vision, enhancing user experience in AR/VR.
    • The developed calibration and gaze extraction algorithm shows significant applicability for future eye-tracking systems.