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Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks.

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    This study introduces a new multiview method for gaze estimation, improving accuracy by simultaneously predicting gaze direction and point. It also presents the largest multiview gaze tracking dataset to date.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Gaze estimation predicts where a person is looking from eye images, crucial for understanding visual attention.
    • Existing methods often use single cameras and focus on either gaze point or direction, not both.

    Purpose of the Study:

    • To develop a novel multitask method for accurate gaze point estimation using multiview cameras.
    • To leverage the relationship between gaze direction and gaze point estimation.

    Main Methods:

    • Proposed a partially shared convolutional neural networks architecture for simultaneous gaze direction and point estimation.
    • Introduced a new, large-scale multiview gaze tracking dataset with diverse subjects.

    Main Results:

    • The proposed multiview multitask approach consistently outperformed existing methods on multiple datasets.
    • Demonstrated superior performance in gaze point estimation accuracy.

    Conclusions:

    • The novel multitask framework effectively integrates gaze direction and point estimation for enhanced accuracy.
    • The new dataset facilitates further research and development in multiview gaze tracking.