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FERDCNN:通过深层卷积神经网络进行面部表情识别的高效方法.
Metwally Rashad1,2, Doaa Alebiary1, Mohammed Aldawsari2
1Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
PeerJ. Computer science
|December 9, 2024
概括
本研究提出了一种高效的面部表情识别 (FER) 方法,使用深 convolutional 神经网络 (DCNNs) 和支持向量机器 (SVMs). FERDCNN方法实现了高精度,证明了其在计算机视觉应用中的有效性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 面部表情识别 (FER) 对许多现实应用至关重要,并且仍然是一个具有挑战性的计算机视觉问题.
- 深度学习,特别是深度卷积神经网络 (DCNNs),为像FER这样的复杂任务提供了有希望的解决方案.
研究的目的:
- 引入一种高效的面部表情识别方法 (FERDCNN),使用预先训练的DCNN模型.
- 评估FERDCNN在识别基本情绪的标准数据集 (CK+和JAFFE) 上的表现.
主要方法:
- 图像预处理包括面部检测,大小调整,马校正和直方形平衡.
- 通过使用五个预训练的DCNN模型提取了深度特征:AlexNet,GoogleNet,ResNet-18,ResNet-50和ResNet-101.
- 转移学习和支持向量机器 (SVM) 用于特征分类.
主要成果:
- 在CK+数据库中,FERDCNN方法的准确率高达99.0%,在JAFFE数据集中达到95.16%.
- 亚历克斯网作为特征提取器表现最好,而SVM作为分类器表现出色.
- 结合AlexNet和SVM,获得了最高的识别准确度.
结论:
- 拟议的FERDCNN方法,特别是AlexNet-SVM组合,对于面部表情识别非常有效.
- 该研究验证了DCNN和SVM在从面部图像准确分类人类情绪方面的效率.
- 这项研究有助于推进FER自动化系统的实际应用.
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