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

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

陈妍^{1, 2,},
吴乐晨^1,,
王聪^3,

1.
湖南工商大学计算机学院长沙 410205
2.
湘江实验室长沙 410205
3.
天津科技大学人工智能学院天津 300457

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

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出版历程
- 收稿日期: 2023-10-20
- 录用日期: 2024-02-20
- 网络出版日期: 2024-05-24

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

CHEN Yan^{1, 2
,},
WU Le-Chen^1
,,
WANG Cong^3
,

1.
School of Computer Science, Hunan University of Technology and Business, Changsha 410205
2.
Xiangjiang Laboratory, Changsha 410205
3.
School of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457

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, authentication scheme design, and internet of things. Corresponding author of this paper

摘要

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和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.
- 微表情识别 /
- 深度学习 /
- 图卷积网络 /
- 多层级融合
Abstract: Micro-expressions are subtle and involuntary changes during emotional expression. Accurate and efficient recognition of these is crucial for the early diagnosis and treatment of mental illnesses. Most of the existing methods often neglect the connections between key facial areas in micro-expressions, making it difficult to capture the subtle changes in small sample image spaces, resulting in low recognition rates. To address this, a micro-expression recognition method is proposed based on a multi-level information fusion network. This method includes a video frame selection strategy based on frequency amplitude, which can select frames with high-intensity expressions from micro-expression videos. Additionally, a multi-level information extraction network using self-attention mechanisms and graph convolutional networks, and a fusion network that incorporates global image information, which can capture the subtle changes of facial micro-expressions from different levels to improve the recognition of specific categories. Experiments on public datasets show that our method effectively improves the accuracy and outperforms other advanced methods.
- Micro-expression recognition /
- deep learning /
- graph convolutional network /
- multi-level fusion

HTML全文

图 1 本文模型的整体框架

Fig. 1 Overall framework of the model

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图 2 人脸关键点检测与节点提取

Fig. 2 Face key point detection and node extraction

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图 3 划分节点和构建面部图

Fig. 3 Divide nodes and build face map

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图 4 按部位学习节点的特征表示

Fig. 4 Learning the feature representation of nodes by site

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图 5 局部节点学习网络

Fig. 5 Network of local node learning

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图 6 整体部位学习网络

Fig. 6 Network of whole part learning

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图 7 图像全局信息学习

Fig. 7 Global information learning of images

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图 8 融合网络

Fig. 8 Fusion network

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图 9 CASME II数据集上的实例

Fig. 9 Examples on the CASME II dataset

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图 10 视频帧的选取

Fig. 10 Selection of video frames

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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

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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

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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	SMIC	CASME II	SAMM
消极	70	88	92
积极	51	32	26
惊讶	43	25	15
总计	164	145	133

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表 2 CASME II、SAMM和MMEW的5分类样本分布

Table 2 Distribution of 5 categorical samples for SMIC, CASME II and SAMM

数据集	CASME II	SAMM	MMEW
快乐	32	26	36
惊讶	25	15	89
厌恶	63	—	72
恐惧	—	—	16
压抑	27	—	—
愤怒	—	57	—
蔑视	—	12	—
其他	99	26	66
总计	246	136	279

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表 3 不同帧数的性能对比

Table 3 Performance comparison for different number of frames

$N$	SAMM (3分类)		MMEW (5分类)
$N$	准确率(%)	UF1	准确率(%)	UF1
0	75.94	0.6462	68.45	0.5732
1	84.96	0.7842	75.26	0.6927
2	89.47	0.8356	81.36	0.7834
3	81.20	0.7381	78.85	0.7225

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表 4 不同卷积核数量下的性能对比

Table 4 Performance comparison with different number of convolutional kernels

卷积核数	SAMM (3分类)		MMEW (5分类)
卷积核数	准确率(%)	UF1	准确率(%)	UF1
0	83.46	0.7429	72.75	0.6341
7	89.47	0.8356	81.36	0.7834
68	87.22	0.8247	77.42	0.7323

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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)
学习网络	准确率 (%)	UF1	准确率 (%)	UF1
局部节点	80.45	0.7252	73.12	0.6968
局部节点加整体部位	86.47	0.8021	80.65	0.7626
局部节点加整体部位加图像全局	89.47	0.8356	81.36	0.7834

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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)
损失函数	w	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

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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 the 3 categorization task on the CASME II and SAMM datasets

方法	CASME II		SAMM
方法	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

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表 8 MEGC2019-CDE协议下的性能比较

Table 8 Performance comparison under the MEGC2019-CDE protocol

方法	SMIC		CASME II		SAMM		MEGC2019-CDE
方法	UF1	UAR	UF1	UAR	UF1	UAR	UF1	UAR
Bi-WOOF	0.5727	0.5829	0.7805	0.8027	0.5211	0.5139	0.6296	0.6227
OFF-ANet	0.6817	0.6695	0.8764	0.8681	0.5409	0.5392	0.7196	0.7096
DIN	0.6645	0.6726	0.8621	0.8560	0.5868	0.5663	0.7322	0.7278
STST-Net	0.6801	0.7013	0.8382	0.8686	0.6588	0.6810	0.7353	0.7605
EMR	0.7461	0.7530	0.8293	0.8209	0.7754	0.7152	0.7885	0.7824
AU-GCN	0.7192	0.7215	0.8798	0.8710	0.7751	0.7890	0.7914	0.7933
ME-PLAN	0.7127	0.7256	0.8632	0.8778	0.7164	0.7418	0.7715	0.7864
本文方法	0.7583	0.7741	0.8849	0.8532	0.8356	0.8194	0.8124	0.8231

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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 the 5 categorization task for the CASME II and SAMM datasets

方法	CASME II		SAMM
方法	准确率(%)	UF1	准确率(%)	UF1
DSSN	71.19	0.7297	57.35	0.4644
Graph-TCN	73.98	0.7246	75.00	0.6985
SMA-STN	82.59	0.7946	77.20	0.7033
MERSiamC3D	81.89	0.8300	68.75	0.6400
AU-GCN	74.27	0.7047	74.26	0.7045
GACNN	81.30	0.7090	88.24	0.8279
AMAN	$75.40$	0.7100	68.85	0.6700
本文方法	83.67	0.8428	81.62	0.7523

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