基于特征融合残留注意网络的闭塞面部表情识别
Yuekun Chen1, Shuaishi Liu1, Dongxu Zhao1
1School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China.
Frontiers in neurorobotics
|September 4, 2023
概括
一个新的特征融合残留注意网络 (FFRA-Net) 通过整合全球和本地特征,有效地识别隐蔽的面部表情. 这种方法显著提高了在具有挑战性的现实世界的场景的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在野生环境中识别面部表情是具有挑战性的,因为遮.
- 现有的方法通常会失去关键的表达特征,因为它们只依赖于全球或本地信息.
研究的目的:
- 提出一个新的特征融合残留注意网络 (FFRA-Net),用于强大的封闭面部表情识别 (FER).
- 加强全球和本地特征的整合,以提高FER准确性.
主要方法:
- 在FFRA-Net中,用于各种全球特征的多尺度模块和用于关键本地特征的本地关注模块.
- 一个特征融合模块集成了这些特征,并使用剩余链接来保存细粒度的细节.
- 从RAF-DB创建了两个新的封闭数据集,FM_RAF-DB和SG_RAF-DB.
主要成果:
- FFRA-Net实现了高准确度:FM_RAF-DB上的77.87%,SG_RAF-DB上的79.50%,RAF-DB上的88.66%,以及FERPLUS上的88.97%.
- 拟议的网络在封闭的FER任务上,与以前的方法相比,显示出更高的性能.
结论:
- FFRA-Net有效地解决了隐蔽面部表情识别的挑战.
- 该方法在现实世界FER应用中显示出强大的适用性和性能.
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