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基于多层级信息融合网络的微表情识别方法

陈妍 吴乐晨 王聪

陈妍, 吴乐晨, 王聪. 基于多层级信息融合网络的微表情识别方法. 自动化学报, 2024, 50(7): 1445−1457 doi: 10.16383/j.aas.c230641
引用本文: 陈妍, 吴乐晨, 王聪. 基于多层级信息融合网络的微表情识别方法. 自动化学报, 2024, 50(7): 1445−1457 doi: 10.16383/j.aas.c230641
Chen Yan, Wu Le-Chen, Wang Cong. A micro-expression recognition method based on multi-level information fusion network. Acta Automatica Sinica, 2024, 50(7): 1445−1457 doi: 10.16383/j.aas.c230641
Citation: Chen Yan, Wu Le-Chen, Wang Cong. A micro-expression recognition method based on multi-level information fusion network. Acta Automatica Sinica, 2024, 50(7): 1445−1457 doi: 10.16383/j.aas.c230641

基于多层级信息融合网络的微表情识别方法

doi: 10.16383/j.aas.c230641
基金项目: 国家自然科学基金(62273140, 72342018) 资助
详细信息
    作者简介:

    陈妍:湖南工商大学计算机学院教授. 主要研究方向为智慧医疗, 健康大数据分析和医疗服务智能管理. E-mail: yanchen@hu.edu.cn

    吴乐晨:湖南工商大学计算机学院硕士研究生. 主要研究方向为深度学习, 计算机视觉. E-mail: lecwu@163.com

    王聪:天津科技大学人工智能学院副教授. 主要研究方向为网络安全, 身份认证和物联网. 本文通信作者. E-mail: wangcongjcdd@tust.edu.cn

A Micro-expression Recognition Method Based on Multi-level Information Fusion Network

Funds: Supported by National Natural Science Foundation of China (62273140, 72342018)
More Information
    Author Bio:

    CHEN Yan Professor at the School of Computer Science, Hunan University of Technology and Business. Her research interest covers smart medical treatment, health big data analysis, and intelligent management of medical services

    WU Le-Chen Master student at the School of Computer Science, Hu-nan University of Technology and Business. His research interest covers deep learning and computer vision

    WANG Cong Associate professor at the School of Artificial Intelligence, Tianjin University of Science and Technology. Her research interest covers network security, identity authentication, and internet of things. Corresponding author of this paper

  • 摘要: 微表情是人类情感表达过程中细微且不自主的表情变化, 实现准确和高效的微表情识别, 对于心理疾病的早期诊断和治疗有重要意义. 现有的微表情识别方法大多未考虑面部产生微表情时各个关键部位间的联系, 难以在小样本图像空间上捕捉到微表情的细微变化, 导致识别率不高. 为此, 提出一种基于多层级信息融合网络的微表情识别方法. 该方法包括一个基于频率幅值的视频帧选取策略, 能从微表情视频中筛选出包含高强度表情信息的图像帧、一个基于自注意力机制和图卷积网络的多层级信息提取网络以及一个引入图像全局信息的融合网络, 能从不同层次捕获人脸微表情的细微变化, 来提高对特定类别的辨识度. 在公开数据集上的实验结果表明, 该方法能有效提高微表情识别的准确率, 与其他先进方法相比, 具有更好的性能.
  • 图  1  本文模型的整体框架

    Fig.  1  Overall framework of our model

    图  2  人脸关键点检测与节点提取

    Fig.  2  Face key point detection and node extraction

    图  3  划分节点和构建面部图

    Fig.  3  Dividing nodes and building face map

    图  4  按部位学习节点的特征表示

    Fig.  4  Learning the feature representation of nodes by site

    图  5  局部节点学习网络

    Fig.  5  Network of local node learning

    图  6  整体部位学习网络

    Fig.  6  Network of whole part learning

    图  7  图像全局信息学习

    Fig.  7  Global information learning of images

    图  8  融合网络

    Fig.  8  Fusion network

    图  9  CASME II数据集上的实例

    Fig.  9  Examples on the CASME II dataset

    图  10  视频帧的选取

    Fig.  10  Selection of video frames

    图  11  本文方法与EMR、AU-GCN的对比结果

    Fig.  11  Comparison results of our method with EMR and AU-GCN

    图  12  多层级信息提取网络的可视化结果

    Fig.  12  Visualization results of multi-level information extraction network

    表  1  SMIC、CASME II和SAMM的3分类样本分布

    Table  1  Distribution of 3 categorical samples for SMIC, CASME II and SAMM

    数据集SMICCASME IISAMM
    消极708892
    积极513226
    惊讶432515
    总计164145133
    下载: 导出CSV

    表  2  CASME II、SAMM和MMEW的5分类样本分布

    Table  2  Distribution of 5 categorical samples for CASME II、SAMM and MMEW

    数据集CASME IISAMMMMEW
    快乐322636
    惊讶251589
    厌恶6372
    恐惧16
    压抑27
    愤怒57
    蔑视12
    其他992666
    总计246136279
    下载: 导出CSV

    表  3  不同帧数的性能对比

    Table  3  Performance comparison for different numbers of frames

    $N$SAMM (3分类)MMEW (5分类)
    准确率(%)UF1准确率(%)UF1
    075.940.646268.450.5732
    184.960.784275.260.6927
    289.470.835681.360.7834
    381.200.738178.850.7225
    下载: 导出CSV

    表  4  不同卷积核数量下的性能对比

    Table  4  Performance comparison with different numbers of convolutional kernels

    卷积核数SAMM (3分类)MMEW (5分类)
    准确率(%)UF1 准确率(%)UF1
    083.460.742972.750.6341
    789.470.835681.360.7834
    6887.220.824777.420.7323
    下载: 导出CSV

    表  5  各网络分支模型的消融实验

    Table  5  Ablation studies of each network branch in our model

    学习网络SAMM (3分类)MMEW (5分类)
    准确率 (%)UF1 准确率 (%)UF1
    局部节点80.450.725273.120.6968
    局部节点加整体部位86.470.802180.650.7626
    局部节点加整体部位加图像全局89.470.835681.360.7834
    下载: 导出CSV

    表  6  ICE的性能验证

    Table  6  Performance validation of ICE

    损失函数 w SAMM (3分类) MMEW (5分类)
    准确率 (%) UF1 准确率 (%) UF1
    CE 85.71 0.7982 79.93 0.7463
    ICE 0.1 87.96 0.8236 80.28 0.7732
    ICE 0.3 89.47 0.8356 79.56 0.7635
    ICE 0.5 88.72 0.8262 80.64 0.7582
    ICE 1.0 87.22 0.8194 81.36 0.7834
    ICE 2.0 86.47 0.8124 78.85 0.7281
    ICE 5.0 89.47 0.8293 80.28 0.7546
    ICE 10.0 87.96 0.8178 81.00 0.7782
    下载: 导出CSV

    表  7  CASME II和SAMM数据集上的3分类任务性能比较

    Table  7  Comparison of the performance of the3-categorization task on the CASME II and SAMM datasets

    方法 CASME II SAMM
    准确率(%) UF1 准确率(%) UF1
    Bi-WOOF 58.80 0.6100 58.30 0.3970
    OFF-ANet 88.28 0.8697 68.18 0.5423
    STST-Net 86.86 0.8382 68.10 0.6588
    Graph-TCN 71.20 0.3550 70.20 0.4330
    GACNN 89.66 0.8695 88.72 0.8188
    本文方法 91.03 0.8849 89.47 0.8356
    下载: 导出CSV

    表  8  MEGC2019-CDE协议下的性能比较

    Table  8  Performance comparison under the MEGC2019-CDE protocol

    方法SMICCASME IISAMMMEGC2019-CDE
    UF1UAR UF1UAR UF1UAR UF1UAR
    Bi-WOOF0.57270.58290.78050.80270.52110.51390.62960.6227
    OFF-ANet0.68170.66950.87640.86810.54090.53920.71960.7096
    DIN0.66450.67260.86210.85600.58680.56630.73220.7278
    STST-Net0.68010.70130.83820.86860.65880.68100.73530.7605
    EMR0.74610.75300.82930.82090.77540.71520.78850.7824
    AU-GCN0.71920.72150.87980.87100.77510.78900.79140.7933
    ME-PLAN0.71270.72560.86320.87780.71640.74180.77150.7864
    本文方法0.75830.77410.88490.85320.83560.81940.81240.8231
    下载: 导出CSV

    表  9  CASME II和SAMM数据集的5分类任务性能比较

    Table  9  Comparison of performance on the5-categorization task for the CASME II and SAMM datasets

    方法CASME IISAMM
    准确率(%)UF1 准确率(%)UF1
    DSSN71.190.729757.350.4644
    Graph-TCN73.980.724675.000.6985
    SMA-STN82.590.794677.200.7033
    MERSiamC3D81.890.830068.750.6400
    AU-GCN74.270.704774.260.7045
    GACNN81.300.709088.240.8279
    AMAN$75.40$0.710068.850.6700
    本文方法83.670.842881.620.7523
    下载: 导出CSV
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  • 收稿日期:  2023-10-20
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