一个改进的面部情绪识别系统使用卷积神经网络优化人类机器人交互的优化
1Department of Mining, Industrial and ICT Engineering (EMIT), Manresa School of Engineering (EPSEM), Universitat Politècnica de Catalunya (UPC), BarcelonaTech, 08242, Manresa, Spain. ravi.raj@upc.edu.
Scientific reports
|November 6, 2025
概括
这项研究表明,使用机器学习和计算机视觉,特别是卷积神经网络 (CNN),用于精确的面部情绪识别 (FER) 的可行性. 开发的软件有效地识别图像和视频中的人类情绪,增强人机交互 (HRI).
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 机器人应用越来越需要复杂的人机交互 (HRI) 来实现有效的功能.
- 计算机视觉,特别是面部情绪识别 (FER),对于提高HRI精度至关重要.
- 识别人类的情绪状态是开发更直观和响应的机器人系统的关键.
研究的目的:
- 开发和评估能够使用计算机视觉和机器学习 (ML) 从图像和视频中识别人类情绪的软件.
- 调查基于FER的情绪表达的培训软件的可行性.
- 为基于CNN的FER系统识别必要的面部手势.
主要方法:
- 利用机器学习技术和数字图像处理管道进行情感识别.
- 开发和训练了用于FER的结合型神经网络 (CNN) 的软件.
- 将拟议模型的性能与已建立的数据集进行比较:FER2013,RAF-DB和CK+.
主要成果:
- 开发的软件证明了在人类手势中识别情绪的可行性.
- 在CK+数据集上实现了大约95%的高准确率.
- 数据集的性能各不相同,FER2013数据集的准确度最低,约为64%.
结论:
- 这项研究证实了CNN对于准确的面部情绪识别的可行性.
- 这些发现有助于推进神经网络的知识和提高计算机视觉效率.
- 增强的FER功能可以显著提高各种应用中人机交互的质量.
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