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MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation.

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    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    This study introduces the MPIIGaze dataset for unconstrained gaze estimation, showing GazeNet improves accuracy by 22% in cross-dataset evaluations.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Unconstrained gaze estimation using learning-based methods shows promise but lacks diverse datasets and cross-dataset validation.
    • Current gaze datasets are limited to laboratory conditions, hindering real-world applicability.

    Purpose of the Study:

    • To address limitations in gaze estimation research by introducing a new large-scale dataset and evaluating existing methods.
    • To propose a novel deep learning model for appearance-based gaze estimation.

    Main Methods:

    • Collected MPIIGaze dataset with 213,659 images from 15 users during daily laptop use, including manual annotations for key facial landmarks.
    • Evaluated state-of-the-art gaze estimation methods across multiple datasets, analyzing factors like image resolution, illumination, and head pose.
    • Developed GazeNet, a deep learning model for appearance-based gaze estimation.

    Main Results:

    • MPIIGaze dataset provides realistic variations in appearance and illumination for robust gaze estimation.
    • Image resolution and binocular input significantly impact gaze estimation accuracy; head pose and pupil data are less critical.
    • GazeNet achieved a 22% improvement in cross-dataset evaluation, reducing mean error from 13.9 to 10.8 degrees.

    Conclusions:

    • The MPIIGaze dataset enables more realistic and challenging evaluations of unconstrained gaze estimation.
    • GazeNet represents a significant advancement in appearance-based gaze estimation accuracy, particularly in cross-dataset scenarios.