观察者的文化背景和目标脸的掩饰条件对机器学习数据集面部表情识别的影响
Masahito Nagata1, Katsunori Okajima1
1Faculty of Environment and Information Sciences, Yokohama National University, Yokohama, Kanagawa, Japan.
PloS one
|October 30, 2024
概括
文化背景和面罩显著改变了面部表情识别 (FER). 东亚观察员与西方人看待"恐惧"的不同,影响受过多数标签训练的自动化系统.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 面部表情识别 (FER) 对人机交互至关重要.
- 现有的机器学习数据集可能无法完全捕捉到对FER的文化和掩饰影响.
- 自动化系统存在风险偏见,因为它忽视了人类多样化的感知.
研究的目的:
- 调查文化背景和面罩对FER的影响.
- 分析FER数据集和参与者响应中的变化.
- 提高自动化FER系统的公平性和细微差别.
主要方法:
- 与东亚参与者进行了FER实验.
- 结果与FERPlus数据集 (西方评级者) 的结果进行了比较.
- 分析了图像/参与者变化和眼睛打开率.
主要成果:
- 东亚人比西方人更多地把"恐惧"解释为"惊喜".
- 掩盖改变了情感感知;对于东亚人来说",恐惧"成了"惊喜".
- 西方人认为的"悲伤"被东亚人视为"恶心".
- 情感标签在掩盖下转移到不同类别,而不仅仅是精确度降低.
结论:
- 根据掩盖和评估者的多样性,FER数据集的特征会有所不同.
- 在多数标签上训练的机器学习模型可能会表现出偏见.
- 纳入人类的变性对于公平和细微的自动化FER至关重要.
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