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Appearance-Based Gaze Estimation With Deep Learning: A Review and Benchmark.

Yihua Cheng, Haofei Wang, Yiwei Bao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    This study reviews deep learning for appearance-based gaze estimation, addressing challenges in comparing methods. It provides a benchmark and guidelines for developing future gaze estimation algorithms.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Human gaze is vital for understanding focus and intent.
    • Deep learning has advanced appearance-based gaze estimation.
    • Lack of standardized guidelines hinders deep learning gaze estimation algorithm development due to comparison inconsistencies.

    Purpose of the Study:

    • To systematically review deep learning-based appearance-based gaze estimation methods.
    • To establish fair comparison metrics and address pre-processing/post-processing variations.
    • To provide a comprehensive benchmark and development guidelines for future research.

    Main Methods:

    • Surveying deep learning gaze estimation algorithms across the pipeline: feature extraction, model design, calibration, and platforms.
    • Summarizing pre-processing and post-processing techniques for fair performance comparison (e.g., face/eye detection, data rectification, 2D/3D gaze conversion).
    • Establishing a benchmark including dataset characterization and source code for typical algorithms.

    Main Results:

    • A systematic review of current deep learning gaze estimation techniques is presented.
    • Standardized methods for pre-processing and post-processing are summarized to enable fair comparisons.
    • A comprehensive benchmark with public datasets and source code is provided.

    Conclusions:

    • This work offers a valuable reference for developing deep learning-based gaze estimation methods.
    • It serves as a guideline to standardize and advance future research in gaze estimation.
    • The benchmark and reviewed methods aim to improve the reliability and comparability of gaze estimation algorithms.