用表情引导的深度联合学习用于面部表情识别
Bei Fang1, Yujie Zhao2, Guangxin Han1
1Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种高效的卷积神经网络 (CNN) 用于面部表情识别,通过利用面部识别数据进行自动注释和实现高精度来减少对大型标记数据集的依赖.
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:
- 卷积神经网络 (CNN) 主导着面部表情识别,但需要广泛的标记数据,并且具有高参数数.
- 现有方法面临的挑战是有限的培训样本和计算开销.
研究的目的:
- 为面部表情识别开发一个高效的CNN框架,尽量减少手动注释数据的需求.
- 提高面部表情识别系统的准确性和效率,特别是用于教育应用.
主要方法:
- 为了降低计算成本,开发了一种使用亲和卷积模块的新型高效CNN.
- 采用表情引导的深度集群方法,自动标记面部识别数据集用于培训.
- 美国有线电视新闻网 (CNN) 使用联合损失函数和新注释的数据集进行了微调.
主要成果:
- 拟议的方法在教育中面部表情识别的自收集数据集上实现了95.87%的准确性.
- 与现有方法相比,该框架在多个具有挑战性的数据集上表现出更高的性能.
- 亲和卷积模块显著降低了计算开销.
结论:
- 开发的深度联合学习框架有效地解决了面部表情识别数据稀缺和参数低效的局限性.
- 这种方法为现实世界的应用提供了有希望的解决方案,例如教育监控,通过允许准确和高效的表达式分析.
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