从面部图像,身体手势和骨姿势的关键点识别情绪:BER2024数据集
Fernando Pujaico Rivera1, Paulo Sergio Rodrigues1, Oscar Eduardo Hidetoshi Fugita2
1Department of Electrical Engineering, University Center of FEI, SBC, SP, Brazil.
Computers in biology and medicine
|May 22, 2025
概括
这项研究引入了用于身体情绪识别的新数据集,实现了从面部和身体图像中分类情绪的高准确性. 这一进步有助于理解非语言线索,以获得更好的医疗保健应用.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 医疗信息学 医疗信息学
背景情况:
- 身体语言对于沟通和情感解释至关重要.
- 准确地将肢体语言分为情感类别是具有挑战性的,因为有限的数据集.
- 现有的数据集往往缺乏对医疗保健等特定背景的关注.
研究的目的:
- 为了解决训练身体语言分类器的数据集稀缺的问题.
- 介绍身体情绪识别数据集,用于评估身体表情分类.
- 在这个新数据集上评估卷积神经网络 (CNN) 模型的性能.
主要方法:
- 使用模拟情绪表达的图像开发了身体情绪识别数据集.
- 员工从预先训练的模型 (ImageNet) 中转移学习.
- 测试了三种CNN方法:面部图像,身体图像和骨关键点.
主要成果:
- 达到高测试准确度:96.25%的面部图像和95.59%的身体图像.
- 骨关键点分析的准确率为67.53%.
- 结果表明了数据集的质量和CNN在情绪分类方面的潜力.
结论:
- 身体情绪识别数据集为训练和评估身体语言分类器提供了坚实的基础.
- 面部和身体图像的分类准确度高于骨数据.
- 该数据集是未来对情感计算和医疗保健研究的宝贵资源.
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