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使用自动化面部动作编码自发面部仿真检测的电肌图验证

Chun-Ting Hsu¹, Wataru Sato¹

¹Psychological Process Research Team, Guardian Robot Project, RIKEN, Soraku-gun, Kyoto 619-0288, Japan.

Sensors (Basel, Switzerland)

|November 25, 2023

概括

自动面部动作编码系统 (FACS) 软件显示了检测自发面部模仿的潜力,与电肌图 (EMG) 相对应,但精度较低. 研究人员应谨慎使用自动化FACS,特别是在临床人群中.

科学领域:

心理学心理学心理学
生物医学工程生物医学工程
计算机科学计算机科学

背景情况:

电肌图 (EMG) 是测量面部模仿的黄金标准.
自动化面部动作编码系统 (FACS) 软件越来越多地用于此目的,但其有效性尚未得到充分证实.
自发的面部模仿是社会互动和情绪处理的关键指标.

研究的目的:

为了验证自动化FACS软件与黄金标准的面部EMG进行验证,以检测自发的面部模仿.
评估自动化FACS在识别特定面部动作单元方面的准确性和灵敏性.
为了比较不同自动化FACS软件 (FaceReader,Py-Feat,OpenFace) 的性能.

主要方法:

参与者观看实时和预先录制的面部表情.
同时获得了面部电磁共振 (zygomaticus大 - ZM) 和面部视频录像.
用FaceReader,Py-Feat和OpenFace分析了面部视频,用于行动单元12 (AU12,唇角拉拉器).
自动化FACS AU12估计与ZM EMG信号进行了比较.

主要成果:

自动化FACS软件检测到与ZM EMG反应相关的自发面部模仿 (AU12).
所有经过测试的软件都检测到与预先录制的视频相比,在现场表演期间有增强的模仿.
自动化FACS表现出比面部EMG更低的灵敏度和准确性,延迟时间为100-300 ms.
在自动化FACS AU12和ZM EMG信号之间发现了显著的相关性.

结论:

自动化FACS软件可以检测自发的面部模仿,但不能取代面部EMG.
该软件适用于在面部模拟中检测大效果大小.
建议在使用自动化FACS时谨慎使用,特别是在临床人群中.
应将EMG验证视为自动化FACS开发和应用的基准.

关键词:

面部阅读器 (FaceReader) 是一个开放的面对面是开放的这就是Py-Feat.交叉相关性交叉相关性电动肌谱学电动肌谱学面部动作编码系统自发的面部模仿.

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