基于CNN的面部表情识别,同时考虑阶级间和阶级内部的变化
Trong-Dong Pham1, Minh-Thien Duong1, Quoc-Thien Ho1
1Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一种新的面部表情识别损失函数. 它通过更好地区分相似的情绪和增强不同情绪之间的差异来提高准确性.
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:
- 面部表情识别对于理解人类情绪和非语言线索至关重要.
- 当前的面部识别技术往往忽视了深度学习模型中损失函数的重要性.
- 现有的方法主要集中在新的网络架构上,忽视了损失函数优化.
研究的目的:
- 为基于卷积神经网络 (CNN) 的面部表情识别引入新的损失功能.
- 同时解决类间和类内部的变化,以提高识别准确度.
- 通过优化损失函数来提高面部表情识别系统的性能.
主要方法:
- 开发了一种新的损失函数,旨在通过将深层特征拉向它们的类中心来最大限度地减少类内变化.
- 通过将深层特征从非对应的类中心推开,并最大限度地提高不同类中心之间的距离,增加了类间的变化.
- 将拟议的损失函数集成到CNN架构中,用于面部表情识别任务.
主要成果:
- 拟议的损失函数与基准数据集上的现有方法相比,显示出更高的性能.
- 根据Cohn-Kanade Plus,Oulu-Casia,MMI和FER2013数据集进行评估,显示了显著的改进.
- 有效地减少了类内变化和增加了类间变化,从而导致更强大的特征表示.
结论:
- 新的损失函数在面部表情识别准确性和效率方面取得了重大进展.
- 这种方法为训练情绪识别的深度学习模型提供了更有效的方法.
- 该方法显示了对需要精确面部表情分析的现实应用的巨大潜力.
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