基于运动放大网络和图表注意力机制的面部微表情识别
Falin Wu1, Yu Xia1, Tiangyang Hu1
1SNARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37, XueYuan Road, HaiDian District, Beijing, 100191, China.
Heliyon
|September 3, 2024
概括
这项研究引入了一个新的网络,通过放大微妙的面部肌肉运动来改善微表情识别. 改进的方法显著提高了从短暂的面部表情中检测真实的情绪的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 微表情揭示了真正的情绪,但由于微妙的面部肌肉运动,很难识别.
- 现有的方法很难准确地检测出这些短暂的情绪指标.
研究的目的:
- 开发一个先进的网络来准确识别微表达式.
- 提高微妙的面部肌肉运动的检测,以改善情绪分析.
主要方法:
- 提出了一个基于Swin变压器的网络 (ST-MEMM),用于微表达运动放大,放大微妙的运动.
- 引入了基于图形注意力机制的网络 (GAM-MER) 以进行识别,优化面部地标和优先考虑关键特征.
- 使用CASME II和SAMM数据集进行实验验证.
主要成果:
- 拟议的基于图形注意力机制的运动放大导向微表情识别网络 (GAM-MM-MER) 显示出高精度.
- 在微表达式识别方面,在现有的最先进的方法上取得了显著的优势.
- 废弃性研究证实了拟议网络的稳定性和有效性.
结论:
- GAM-MM-MER网络有效地增强了微妙的肌肉运动,并专注于关键的面部地标.
- 拟议的方法在微表情识别领域提供了显著的进步.
- 该网络被证明是强大而有效的,可以从微表情中准确识别真实的情绪.
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