基于CNN-LSTM的情绪识别,使用切比舍夫时刻和K折验证与多图书馆SVM
Samanthisvaran Jayaraman1, Anand Mahendran2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
PloS one
|April 7, 2025
概括
这项研究引入了一种混合CNN-LSTM模型,用于分析驾驶员的面部表情,以检测情绪状态并提高驾驶安全. 与现有的混合方法相比,拟议的模型实现了优越的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 人类的情绪和面部表情缺乏直接,一致的相关性.
- 面部表情受到心理因素的影响,影响其表现.
- 机器学习和神经网络越来越多地用于分析复杂的人类行为.
研究的目的:
- 分析驾驶员的面部表情,以检测情绪和情绪状态.
- 通过了解驾驶员的情绪来提高道路安全.
- 为准确的情感识别提出和评估一种新的混合模型.
主要方法:
- 混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型的开发.
- 集成RESNET152 CNN与多图书馆支持向量机 (SVM) 进行分类.
- 利用切比舍夫时刻来增强特征提取和K折验证以进行性能评估.
主要成果:
- 拟议的混合CNN-LSTM模型在传统方法上表现出优越的性能.
- 该模型在分类驾驶员情绪状态方面取得了很高的准确性.
- 在这种情况下,切比舍夫时刻对特征提取有效.
结论:
- 混合CNN-LSTM模型为实时驾驶员情绪识别提供了一个有希望的方法.
- 精确的情绪检测可以大大提高道路安全.
- 进一步的研究可以探索更复杂的情绪状态和不同的驾驶条件.
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