三CAFFNet:一个三交叉注意力变压器与多功能融合网络面部表情识别
Yuan Tian1, Zhao Wang1, Di Chen1
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了TriCAFFNet,这是一个用于在现实环境下识别面部表情的新型网络. 该模型通过融合多个特征并使用三十字注意力机制来提高准确性.
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 (FER) 已经进步,但实际应用仍然具有挑战性.
- 现有的方法在微妙的表达和不同的环境条件下扎.
研究的目的:
- 提出一个先进的模型,TriCAFFNet,用于在具有挑战性的环境中强大的面部表情识别.
- 提高FER系统的准确性和可靠性.
主要方法:
- 开发了TriCAFFNet,这是一个多功能融合网络,包含本地二进制模式 (LBP),定向梯度 (HOG) 的直方图,地标和卷积神经网络 (CNN) 功能.
- 实施了三重交叉注意力块,以实现功能交互和相互指导,以捕捉突出的注意力.
主要成果:
- 在基准数据集上实现了最先进的 (SOTA) 性能.
- 在RAF-DB上获得了92.17%的准确性,在AffectNet (7类) 上获得了67.40%的准确性,在AffectNet (8类) 上获得了63.49%的准确性.
结论:
- 在面部表情识别方面,TriCAFFNet表现出卓越的性能,特别是在具有挑战性的现实环境下.
- 拟议的多功能融合和注意力机制是有效提高FER准确性的.
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