首页|基于颜色和光流的多注意力机制微表情识别

基于颜色和光流的多注意力机制微表情识别

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对光流法无法充分利用微表情面部颜色信息,导致识别准确率不高的问题,本文提出一种基于颜色和光流的多注意力双流网络方法.首先,提出以CIE Luv色差图的形式,初步提取人脸情感生理特征,弥补微表情光流特征的单一性和局限性;然后,将PAM模块和ECA block并行组合得到轻量化的双注意力模块,提取空间和通道关键特征;最后,设计一种交叉注意力机制以获取颜色和光流混合特征,将其与空间通道关键特征融合用于分类.本模型在实验中采用留一交叉验证法进行评估,在SAMM数据集上的准确率和F1分数分别达到69.18%和67.04%,在CASME Ⅱ数据集上的准确率和F1分数分别达到72.38%和70.85%.实验结果均优于目前主流算法,进一步证明本文模型及其模块在识别微表情方面的有效性.
Multi-attention micro-expression recognition based on color and optical flow
The optical flow method cannot fully exploit the facial color information of micro-expressions,resulting in low recognition accuracy.Therefore,this paper proposes a multi-attention dual-flow network method based on color and optical flow.Firstly,the facial color difference maps are obtained in the CIE Luv color space,and the emotional-physiological features are extracted to compensate for the singularity and limitation of the micro-expression optical flow features.Then,the PAM module and ECA block are combined in parallel to obtain the lightweight dual-attention module,which extracts the spatial and channel key features.Finally,a cross-attention mechanism is designed to obtain mixed features of color and optical flow.The mixed features are fused with spatial channel key features for micro-expression classification.The model is evaluated experimentally using leave-one-out cross-validation.The accuracy and F1 scores reach 69.18%and 67.04%on the SAMM dataset,and 72.38%and 70.85%on the CASME Ⅱ dataset.The experimental results are superior to the current mainstream algorithms,further proving the effectiveness of the proposed model and its modules in micro-expression recognition.

computer visionmicro-expression recognitionCIE Luvcolor featuresoptical flow featurestwo-stream network

黄凯、王峰、王晔、常亦婷

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太原理工大学 电子信息与光学工程学院,山西 晋中 030606

太原理工大学 电气与动力工程学院,山西 太原 030024

计算机视觉 微表情识别 CIE Luv 颜色特征 光流特征 双流网络

山西省回国留学人员科研资助项目山西省留学回国人员科技活动择优资助基金山西省基础研究计划国家级重点支持领域大创项目

2020-042202000172021030212318620220058

2024

10.37188/CJLCD.2023-0225

液晶与显示
中科院长春光学精密机械与物理研究所 中国光学光电子行业协会液晶分会 中国物理学会液晶分会

液晶与显示

CSTPCD北大核心
影响因子：0.964
ISSN：1007-2780
年,卷(期)：2024.39(7)
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