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

SUN Qiang; CHEN Yuan

doi:10.11999/JEIT231110

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 574-587

SUN Qiang, CHEN Yuan. Bimodal Emotion Recognition With Adaptive Integration of Multi-level Spatial-Temporal Features and Specific-Shared Feature Fusion[J]. Journal of Electronics & Information Technology, 2024, 46(2): 574-587. doi: 10.11999/JEIT231110

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SUN Qiang, CHEN Yuan. Bimodal Emotion Recognition With Adaptive Integration of Multi-level Spatial-Temporal Features and Specific-Shared Feature Fusion[J]. Journal of Electronics & Information Technology, 2024, 46(2): 574-587. doi: 10.11999/JEIT231110

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

doi: 10.11999/JEIT231110

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)

Received Date: 2023-10-11
Rev Recd Date: 2024-01-29

Available Online: 2024-02-02

Publish Date: 2024-02-29

Abstract

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

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References(45)

References

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