改进了具有深度学习模型的优化器,用于情绪检测和分类
C Willson Joseph1,2, G Jaspher Willsie Kathrine1, Shanmuganathan Vimal3
1Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
Mathematical biosciences and engineering : MBE
|August 23, 2024
概括
这项研究引入了一个先进的深度学习框架,用于准确的面部情绪识别 (FER). 新的EWDL-BFSN模型显著提高了情绪检测准确度,在基准数据集上表现优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部情绪识别 (FER) 对于人机交互和生物识别等应用至关重要.
- 当前的FER方法经常在准确性和高错误率方面扎.
- 需要强大而精确的自动化面部情绪检测系统.
研究的目的:
- 开发一个创新的深度学习框架,以海为基础的扩展深度学习与Botox特征选择网络 (EWDL-BFSN),用于准确的面部情绪识别.
- 通过优化特征选择和分类器超参数来自动识别面部情绪.
- 提高FER系统的性能,超出当前最先进的能力.
主要方法:
- 该EWDL-BFSN框架集成的梯度波波异型波器 (GWAF) 的图像预处理和SqueezeNet的特征提取.
- 改进的Botox优化算法 (IBoA) 用于最佳的特征选择.
- 一个基于增强优化的内核剩余50 (EK-ResNet50) 网络执行FER和分类,其超参数由海优化算法 (WOA) 调整.
主要成果:
- 根据EWDL-BFSN模型,CK+数据集的准确率高达99.37%,FER-2013数据集的准确率高达99.25%.
- 分析了包括精度,灵敏度,特异性和F1分数在内的性能指标.
- 拟议的模型在面部情绪预测方面,与现有的最先进的方法相比,表现出了更高的性能.
结论:
- EWDL-BFSN框架为面部情绪识别提供了一个高度准确和有效的解决方案.
- 集成先进的深度学习技术和优化算法显著提高了FER的能力.
- 该模型的卓越性能验证了其在需要精确情绪检测的现实应用中的潜力.
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