探索影响基于网络摄像机的自动注视编码的因素
Hiromichi Hagihara1,2,3,4, Lorijn Zaadnoordijk5, Rhodri Cusack5
1Graduate School of Human Sciences, Osaka University 1-2 Yamadaoka, Suita-shi Osaka, 565-0871, Japan. hiromichi.h@gmail.com.
Behavior research methods
|May 1, 2024
概括
在线实验增强了行为研究. 这项研究创建了一个网络摄像机数据集,以提高婴儿和成人自动目光分类的准确性,解决家庭研究中的噪音因素.
科学领域:
- 行为研究是行为研究.
- 发展心理学是发展心理学.
- 人与计算机的互动.
背景情况:
- 在线实验为行为研究提供了诸如更大的样本大小和多样化的群体等好处.
- 在婴儿研究中,目光行为是关键的结果指标,但手动编码是劳动密集的.
- 从网络摄像机数据中自动分类眼神可以简化婴儿研究,但面临着不受控制的环境带来的挑战.
研究的目的:
- 创建一个受控的成人网络摄像头数据集,复制婴儿在线研究中的噪音因素.
- 为了评估特定噪声因素对自动化面部和目光检测算法的影响.
- 评估面部匿名化在在线行为研究中的数据共享的可行性.
主要方法:
- 系统变化的因素:左右偏移,相机距离,面部旋转和照明方向.
- 使用了两个最先进的分类算法 (iCatcher+和OWLET).
- 测试了变形面孔对算法性能不可识别的影响.
主要成果:
- 面部检测对照明条件最敏感.
- 视线编码的准确性一直受到相机距离和照明的影响.
- 面部匿名化并没有显著降低性能,这表明它对数据透明度的实用性.
结论:
- 摄像机距离和照明是影响在线研究中目光编码精度的关键因素.
- 面部匿名化是共享在线行为数据的可行选择.
- 开发的数据集和研究结果将提高发展心理学研究在线测试的稳定性.
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