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在观看情感唤起的视频时,观众的面部关键点的持续移动
Shivansh Chandra Tripathi1, Rahul Garg1,2,3
1Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.
PloS one
|May 17, 2024
概括
研究人员开发了一种使用面部表情来测量不同个体之间一致的大脑反应的新方法. 这种具有成本效益的方法,即平均t统计,为研究大脑功能和参与提供了fMRI和EEG的灵活替代方案.
科学领域:
- 神经心理学 神经心理学
- 认知神经科学 认知神经科学
- 计算机视觉 计算机视觉
背景情况:
- 神经心理学研究研究了暴露于相同刺激的个体之间共享的大脑功能.
- 通过fMRI,EEG和生理信号观察到的一致反应量化了共享的大脑活动,称为一致响应测量 (CRM).
- 现有的CRM方法,如fMRI是昂贵的,而EEG和生理信号容易受到文物和环境因素的影响.
研究的目的:
- 通过使用面部表情视频,为一致响应测量 (CRM) 引入一个具有成本效益和灵活性的替代方案.
- 通过自动化面部关键点跟踪来量化CRM,提出一个新的指标,即平均t统计.
- 为了验证平均t统计数与直接面部肌肉运动测量的相关性.
主要方法:
- 利用计算机视觉和面部表情视频上的自动面部关键点跟踪.
- 介绍了平均t统计作为CRM的新型指标,类似于主体间相关性 (ISC).
- 使用DISFA数据集从面部动作编码系统 (FACS) 获得的面部肌肉运动指标与平均t统计结果相关联.
主要成果:
- 平均t统计结果显示,与AU一致性有很强的相关性 (R2 = 0.78),AU一致性是通过FACS直接测量面部肌肉运动的直接指标.
- 用平均t统计数据分析的面部表情视频,提供了一个受外部条件影响最小的CRM方法.
- 与fMRI和EEG相比,拟议的方法为CRM提供了更简单,更容易获得的方法.
结论:
- 从面部表情获得的平均t统计是一致响应测量 (CRM) 的有效和有效指标.
- 这种自动化面部分析技术扩展了CRM应用在诸如在线学习参与,客户互动分析和临床诊断 (如中风,自闭症,抑郁症) 等领域.
- 代码库的公共可用性旨在促进使用面部表情的自动化CRM的进一步研究.
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