多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别

孙强; 陈远

doi:10.11999/JEIT231110

多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别

doi: 10.11999/JEIT231110

孙强^{1, 2, ,},
陈远¹

1.
西安理工大学自动化与信息工程学院通信工程系西安 710048
2.
西安市无线光通信与网络研究重点实验室西安 710048

基金项目: 西安市科技计划项目(22GXFW0086)，西安市碑林区科技计划项目(GX2243)，西安理工大学研究生校企协同创新基金 (310/252062108)

详细信息

作者简介:
孙强：男，博士，副教授，研究方向为情感计算、智慧气象与人机交互

陈远：男，硕士生，研究方向为脑电-人脸图像双模态情感识别

通讯作者:
孙强　qsun@xaut.edu.cn

中图分类号: TN911.7; TP391
计量
- 文章访问数: 212
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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-11
- 修回日期: 2024-01-29
- 网络出版日期: 2024-02-02
- 刊出日期: 2024-02-10

Bimodal Emotion Recognition With Adaptive Integration of Multi-level Spatial-Temporal Features and Specific-Shared Feature Fusion

SUN Qiang^{1, 2
, ,},
CHEN Yuan¹

1.
Department of Communication Engineering, School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
2.
Xi’an Key Laboratory of Wireless Optical Communication and Network Research, Xi’an 710048, China

Funds: The Science and Technology Project of Xi’an City (22GXFW0086), The Science and Technology Project of Beilin District in Xi’an City (GX2243), The School-Enterprise Collaborative Innovation Fund for Graduate Students of Xi’an University of Technology (310/252062108)

摘要

摘要: 在结合脑电(EEG)信号与人脸图像的双模态情感识别领域中，通常存在两个挑战性问题：(1)如何从EEG信号中以端到端方式学习到更具显著性的情感语义特征；(2)如何充分利用双模态信息，捕捉双模态特征中情感语义的一致性与互补性。为此，提出了多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别模型。一方面，为从EEG信号中获得更具显著性的情感语义特征，设计了多层次时空特征自适应集成模块。该模块首先通过双流结构捕捉EEG信号的时空特征，再通过特征相似度加权并集成各层次的特征，最后利用门控机制自适应地学习各层次相对重要的情感特征。另一方面，为挖掘EEG信号与人脸图像之间的情感语义一致性与互补性，设计了特有-共享特征融合模块，通过特有特征的学习和共享特征的学习来联合学习情感语义特征，并结合损失函数实现各模态特有语义信息和模态间共享语义信息的自动提取。在DEAP和MAHNOB-HCI两种数据集上，采用跨实验验证和5折交叉验证两种实验手段验证了提出模型的性能。实验结果表明，该模型取得了具有竞争力的结果，为基于EEG信号与人脸图像的双模态情感识别提供了一种有效的解决方案。
- 双模态情感识别 /
- 脑电 /
- 人脸图像 /
- 多层次时空特征 /
- 特征融合
Abstract: There are usually two challenging issues in the field of bimodal emotion recognition combining ElectroEncephaloGram (EEG) and facial images: (1) How to learn more significant emotionally semantic features from EEG signals in an end-to-end manner; (2) How to effectively integrate bimodal information to capture the coherence and complementarity of emotional semantics among bimodal features. In this paper, a bimodal emotion recognition model is proposed via the adaptive integration of multi-level spatial-temporal features and the fusion of specific-shared features. On the one hand, in order to obtain more significant emotionally semantic features from EEG signals, a module, called adaptive integration of multi-level spatial-temporal features, is designed. The spatial-temporal features of EEG signals are firstly captured with a dual-flow structure before the features from each level are integrated by taking into consideration the weights deriving from the similarity of features. Finally, the relatively important feature information from each level is adaptively learned based on the gating mechanism. On the other hand, in order to leverage the emotionally semantic consistency and complementarity between EEG signals and facial images, one module fusing specific-shared features is devised. Emotionally semantic features are learned jointly through two branches: specific-feature learning and shared-feature learning. The loss function is also incorporated to automatically extract the specific semantic information for each modality and the shared semantic information among the modalities. On both the DEAP and MAHNOB-HCI datasets, cross-experimental verification and 5-fold cross-validation strategies are used to assess the performance of the proposed model. The experimental results and their analysis demonstrate that the model achieves competitive results, providing an effective solution for bimodal emotion recognition based on EEG signals and facial images.
- Bimodal emotion recognition /
- ElectroEncephaloGram (EEG) /
- Facial image /
- Multi-level spatial-temporal features /
- Feature fusion

HTML全文

图 1 提出模型架构示意图

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图 2 AIMSTF模块结构示意图

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图 3 Zhang等人^[14]方法结构示意图

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图 4 SSFF模块结构示意图

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图 5 所用数据集中各个受试者的准确率

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图 6 在DEAP数据集上AIMSTF模块对模型性能的影响

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图 7 在DEAP和MAHNOB-HCI数据集上利用5折交叉验证时提出模型产生的t-SNE可视化图

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表 1 Zhang等人^[14]所使用的预训练CNN的详细结构参数

网络层名称	详细参数	步长	激活函数	输出大小
输入	\	\	\	$ 3 \times 64 \times 64 $
卷积层1	$ 32@3 \times 3 $	1	ReLU	$ 32 \times 62 \times 62 $
最大池化1	$ 3 \times 3 $	2	\	$ 32 \times 30 \times 30 $
卷积层2 最大池化2	$ 64@3 \times 3 $	1	ReLU	$ 64 \times 28 \times 28 $
卷积层2 最大池化2	$ 3 \times 3 $	2	\	$ 64 \times 13 \times 13 $
卷积层3	$ 128@3 \times 3 $	1	ReLU	$ 128 \times 13 \times 13 $
最大池化3	$ 3 \times 3 $	2	\	$ 128 \times 6 \times 6 $

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表 2 提出模型的详细结构参数

模型组成	模块名称	输入特征维度	输出特征维度
EEG特征学习	EEG输入	\	$ 32 \times 128 $
	多尺度卷积	$ 32 \times 128 $	$ 32 \times 136 $
	$ \left[ \begin{array}{c}时空特征联合学习\\ 多层次特征自适应集成\end{array} \right]\times 6 $	$ 32 \times 136 $	$ 32 \times 136 $
		$ 32 \times 136 $, $ 32 \times 136 $	$ 32 \times 136 $
	Flatten	$ 32 \times 136 $	$ 1 \times 4352 $
	全连接层	$ 1 \times 4352 $	$ 1 \times 128 $
Face特征学习	Face输入	\	$ 5 \times 3 \times 64 \times 64 $
	预训练CNN	$ 3 \times 64 \times 64 $	$ 32 \times 30 \times 30 $
		$ 32 \times 30 \times 30 $	$ 64 \times 13 \times 13 $
		$ 64 \times 13 \times 13 $	$ 128 \times 6 \times 6 $
	ConvLSTM	$ 5 \times 128 \times 6 \times 6 $	$ 128 \times 6 \times 6 $
	Flatten	$ 128 \times 6 \times 6 $	$ 1 \times 4608 $
	全连接层	$ 1 \times 4608 $	$ 1 \times 128 $
SSFF	EEG特有特征学习	$ 1 \times 128 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 32 $
	Face特有特征学习	$ 1 \times 128 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 32 $
	EEG共享特征学习	$ 1 \times 128 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 32 $
	Face共享特征学习	$ 1 \times 128 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 64 $
		$ 1 \times 64 $	$ 1 \times 32 $
	全连接层	$ 1 \times 32 $,$ 1 \times 32 $	$ 1 \times 32 $
	权重生成网络	$ 1 \times 32 $, $ 1 \times 32 $, $ 1 \times 32 $	$ 1 \times 32 $
		$ 1 \times 32 $	$ 1 \times 1 $
		$ 1 \times 32 $	$ 1 \times 1 $
		$ 1 \times 32 $	$ 1 \times 1 $
	全连接层，丢弃层	$ 1 \times 32 $, $ 1 \times 32 $, $ 1 \times 32 $	$ 1 \times 48 $
	全连接层，丢弃层	$ 1 \times 48 $	$ 1 \times 48 $
	全连接层	$ 1 \times 48 $	$ 1 \times 2 $或$ 1 \times 4 $

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表 3 提出模型与其他方法的性能对比

数据集	验证方法	作者	模态	准确率(%)
数据集	验证方法	作者	模态	Valence	Arousal	Emotion
DEAP	跨实验验证	Huang等人^[39]	EEG, Face	80.30	74.23	\
		Zhu等人^[40]	EEG, Face	72.20	78.84	\
		Li等人^[41]	EEG, Face	71.00	58.75	\
		Zhang等人^[14]	EEG, Face	77.63*	79.21*	60.63*
		本文方法	EEG, Face	82.60	83.09	67.50
	5折交叉验证	Zhang等人^[42]	EEG, PPS	90.04	93.22	\
		Chen等人^[43]	EEG, PPS	93.19	91.82	\
		Wu等人^[44]	EEG, Face	95.30	94.94	\
		Wang等人^[45]	EEG, Face	96.63	97.15	\
		Zhang等人^[14]	EEG, Face, PPS	95.56	95.33	\
		本文方法	EEG, Face	98.21	98.59	90.56
MAHNOB-HCI	跨实验验证	Huang等人^[39]	EEG, Face	75.63	74.17	\
		Li等人^[41]	EEG, Face	70.04	72.14	\
		Zhang等人^[14]	EEG, Face	73.55*	74.34*	54.21*
		本文方法	EEG, Face	79.99	78.60	62.42
	5折交叉验证	Salama等人^[12]	EEG, Face	96.13	96.79	\
		Zhang等人^[14]	EEG, Face, PPS	96.82	97.79	\
		Chen等人^[43]	EEG, PPS	89.96	90.37	\
		Wang等人^[45]	EEG, Face	96.69	96.26	\
		本文方法	EEG, Face	97.02	97.36	88.77
注：PPS(Peripheral Physiological Signal): 外周生理信号

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表 4 DEAP数据集上不同模态配置对模型准确率的统计结果(%)

模态	Valence	Arousal	Emotion
EEG	80.77	80.44	64.43
Face	79.19	79.02	63.22
EEG+Face	82.60	83.09	67.50

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表 5 DEAP数据集上SSFF模块消融实验结果(%)

消融操作	Valence	Arousal	Emotion
去除SSFF	77.81	79.68	59.78
去除共享特征学习分支	80.35	80.93	62.13
去除特有特征学习分支	81.52	81.22	64.46
去除权重生成网络	81.79	82.37	66.25
本文方法	82.60	83.09	67.50

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表 6 DEAP数据集上模型训练所用损失函数消融实验结果(%)

消融操作	Valence	Arousal	Emotion
去除$ {L_{\text{d}}} $	81.68	82.20	64.07
去除$ {L_{\text{s}}} $	82.01	82.43	64.53
去除$ {L_{\text{s}}} $, $ {L_{\text{d}}} $	81.23	81.71	63.12
本文方法	82.60	83.09	67.50

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