面部表情识别基于微调的频道空间注意力转换器
Huang Yao1, Xiaomeng Yang1, Di Chen1
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新模型,即微调通道空间注意力转换器 (FT-CSAT),以提高面部表情识别 (FER) 在现实环境中的准确性. FT-CSAT模型表现出卓越的性能,特别是在遮蔽面部和各种头部姿势方面.
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:
- 面部表情识别 (FER) 对人机交互至关重要.
- 现实世界的FER受到诸如闭塞和头部姿势变化等因素的挑战.
- 现有的方法与这些现实世界的复杂性作斗争.
研究的目的:
- 开发一种先进的模型,用于在野生环境中准确识别面部表情.
- 解决目前FER系统在不受控制的环境中的局限性.
- 为了提高FER的强度,防止遮和头部姿势变化.
主要方法:
- 提出了一个新型模型:微调的通道空间注意力变压器 (FT-CSAT).
- 整合了一个通道空间注意模块,以专注于相关的面部特征.
- 利用微调策略来优化性能和管理参数.
主要成果:
- 在基准数据集上实现了高精度:88.61%在RAF-DB和89.26%在FERPlus.
- 在Occlusion-RAF-DB和Pose-RAF-DB数据集上表现出卓越的性能.
- FT-CSAT有效地处理面部遮和头部姿势变化.
结论:
- FT-CSAT模型显著提升了面部表情识别的最新技术.
- 建议的注意力机制和微调方法在现实场景中提高了稳定性.
- 在具有挑战性的条件下,FT-CSAT为可靠的FER提供了一个有希望的解决方案.
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