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GLFNet:基于注意力机制的全球-本地特征融合网络,用于微表达式识别

Meng Zhang^1,2, Long Yao^1,2, Wenzhong Yang^1,2

¹School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.

Entropy (Basel, Switzerland)

|October 28, 2025

概括

本研究引入了一个全球-本地特征融合网络 (GLFNet),以改进微表达式识别 (MER). GLFNet有效地提取特征,在基准数据集上表现优于现有方法.

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:

微表情是微妙的面部动作,揭示真正的情绪.
微表达式识别 (MER) 具有挑战性,因为数据持续时间短,强度低,数据不平衡.
现有的MER方法在有效的特征提取和数据不平衡方面扎.

研究的目的:

提出一个全新的全球-本地特征融合网络 (GLFNet),以加强MER.
为了应对MER中微妙的移动,低强度和不平衡的数据集的挑战.
提高微表情识别系统的准确性和稳定性.

主要方法:

开发了GLFNet与全球关注 (LA),本地块 (GB) 和自适应特征融合 (AFF) 模块.
采用了注意力机制,以整合全球-本地特征,并强调突出的地方运动.
引入了一个类平衡损失函数,以减轻数据集不平衡问题.

主要成果:

在SMIC,CASME II和SAMM数据集上,GLFNet在最先进的方法上表现优越.
在未加权F1得分方面取得了显著改善:4.67% (SMIC),2.02% (SAMM),0.49% (CASME II).
在MER中验证了全球-本地特征融合战略的有效性.

结论:

拟议的GLFNet有效地提取了MER的歧视性特征.
全球-本地特征融合策略显著提高了微表达式识别精度.
GLFNet代表了微表达式分析领域的重大进步.

关键词:

适应性特征融合融合是一种适应性特征.注意力机制注意力机制阶级不平衡阶级不平衡全球特征全球特征地方特征地方特征地方特征识别微表情的功能

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