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基于外表的目光估计与深度学习:一个审查和基准.

Yihua Cheng, Haofei Wang, Yiwei Bao

    IEEE transactions on pattern analysis and machine intelligence
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    概括

    这项研究回顾了基于外表的目光估计的深度学习,解决了比较方法的挑战. 它为开发未来的目光估计算法提供了一个基准和指导方针.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人与计算机的交互

    背景情况:

    • 人类的目光对于理解焦点和意图至关重要.
    • 深度学习已经推进了基于外表的目光估计.
    • 由于缺乏标准化的指导方针,由于比较不一致,阻碍了深度学习凝视估计算法的开发.

    研究的目的:

    • 系统地审查基于深度学习的基于外观的目光估计方法.
    • 建立公平的比较指标,并解决处理前/处理后变化的问题.
    • 为未来的研究提供全面的基准和发展指南.

    主要方法:

    • 调查整个管道的深度学习目光估计算法:特征提取,模型设计,校准和平台.
    • 总结前处理和后处理技术,以便公平的性能比较 (例如,面部/眼睛检测,数据纠正,2D/3D视线转换).
    • 建立一个基准,包括数据集的表征和典型算法的源代码.

    主要成果:

    • 本文对当前深度学习的目光估计技术进行了系统审查.
    • 总结了预处理和后处理的标准化方法,以便进行公平的比较.
    • 提供了一个包含公共数据集和源代码的全面基准.

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    结论:

    • 这项工作为开发基于深度学习的目光估计方法提供了宝贵的参考.
    • 它作为标准化和推进眼神估计未来研究的指导方针.
    • 该基准和审查的方法旨在提高凝视估计算法的可靠性和可比性.