使用深度学习分析在吃饭时面部情绪表达的分析
Elif Yildirim1, Fatma Patlar Akbulut1, Cagatay Catal2
1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey.
概括
这项研究使用深度学习来分析饮食中的情绪,发现音乐和视频等外部因素显著影响情绪. 面部表情识别实现了77.68%的准确性,为传统调查提供了替代方案.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 神经科学是一个神经科学.
背景情况:
- 吃饭是一种由各种因素影响的情感和社会活动.
- 吃饭时的情绪会影响诸如肥胖等健康状况.
- 评估与饮食相关的情绪是一个多学科的挑战.
研究的目的:
- 通过使用深度学习,自动评估在吃饭时的情绪体验.
- 开发基于面部表情的模型,以减少用户偏见和情绪评估中的错误.
- 调查外部因素 (视频,音乐) 在吃饭时对情绪的影响.
主要方法:
- 利用移动应用程序捕捉神经,行为和身体情绪表现.
- 通过三个卷积神经网络 (CNN) 架构使用面部表情识别:微调的VGG16和Deepface.
- 在三个条件下测试了情绪反应:观看视频,听音乐或在吃饭时不做任何事情.
主要成果:
- 发现外部因素,而不仅仅是食物,在吃饭时显著影响情绪变化.
- 微调的VGG16模型在识别四种不同的情绪方面达到77.68%的准确性.
- 开发的系统提供了一个潜在的替代品主观的基于调查的食品评估方法.
结论:
- 面部表情分析提供了一个客观的方法来评估在吃饭时的情绪.
- 环境刺激在调节饮食的情感体验方面起着至关重要的作用.
- 深度学习模型显示,对与食物相关的情绪进行自动,公正的评估是有前途的.
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