开发基于机器学习的面部热图像分析,用于动态情绪传感
Budu Tang1,2, Wataru Sato1,2, Yasutomo Kawanishi3
1Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8507, Japan.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
机器学习使用像素级面部热成像准确估计情绪兴奋,优于传统方法. 这种非侵入性技术揭示了与情绪状态相关的非线性温度模式.
科学领域:
- 热图学 热图学 热图学 热图学
- 情感计算是一种情感计算.
- 机器学习 机器学习
背景情况:
- 面部热模式与情绪状态相关.
- 以前对面部热数据 (感兴趣的区域) 的线性分析可能错过了复杂的非线性信息.
- 精确的情绪感知对于各种应用非常有价值.
研究的目的:
- 研究面部热图像的像素级分析的机器学习 (ML).
- 用 ML 估计动态情绪兴奋评级.
- 将ML性能与传统线性回归模型进行比较.
主要方法:
- 收集了来自20名观看情感激发电影的参与者的面部热量数据.
- 使用的ML模型:随机森林回归,支持矢量回归,ResNet-18和ResNet-34.
- 使用ResNet-34.4的突出度图和集成梯度来解释非线性关系.
主要成果:
- 在估计兴奋时,ML模型的表现明显优于线性回归.
- ResNet-18和ResNet-34显示出卓越的性能. 它们的性能非常出色.
- 在鼻尖,额头和脸的兴奋和温度变化之间发现了非线性关联.
结论:
- 基于ML的面部热图像的像素级分析对于估计情绪兴奋是有效的.
- 非线性热模式为情绪状态提供了宝贵的见解.
- 潜在的应用包括在心理健康,教育和人机交互方面进行非侵入性情绪感知.
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