Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human–Robot Interaction
Abstract
A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is not completely independent of each other, canonical correlation analysis (CCA) is used to analyze and extract the correlation between the emotion features, and a coupling network is established for emotion recognition of the extracted bi-modal features. Both simulation and application experiments are completed. According to the simulation experiments completed on the bimodal face and body gesture database (FABO), the recognition rate of the proposed method has increased by 1.15% compared to that of the support vector machine recursive feature elimination (SVMRFE) (without considering the unbalanced contribution of features). Moreover, by using the proposed method, the multimodal recognition rate is 21.22%, 2.65%, 1.61%, 1.54%, and 0.20% higher than those of the fuzzy deep neural network with sparse autoencoder (FDNNSA), ResNet-101 + GFK, C3D + MCB + DBN, the hierarchical classification fusion strategy (HCFS), and cross-channel convolutional neural network (CCCNN), respectively. In addition, preliminary application experiments are carried out on our developed emotional social robot system, where emotional robot recognizes the emotion…
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EMERSK's bimodal method outperforms the closest reported result by CMEFA [31] by a margin of 3.15%.
“Specifically, our bimodal method outperforms the closest reported result by CMEFA [31] by a margin of 3.15%.”
From EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge (2023) · cites this paper as [31]
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- Authors
- Chen, L., Li, M., Wu, M., Pedrycz, W., & Hirota, K.
- Affiliations
- China University of Geosciences (CN)
- University of Alberta (CA)
- Tokyo Institute of Technology (JP)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2024-07
- Volume
- 35
- Issue
- 7
- Pages
- 9663-9673
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W4320004356
- Cited by
- 53
- References
- 43
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 7 · 2023: 6
- FWCI
- 13.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61973286, 61603356, 61773353)
- 111 Project (B17040)
- Fundamental Research Funds for the Central Universities, China University of Geosciences (2021063)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- multimodal emotion recognition, human-robot interaction, facial expression recognition, body gesture recognition, canonical correlation analysis
- MeSH terms
- Deep Learning, Algorithms, Emotions, Facial Expression, Gestures, Humans, Pattern Recognition, Automated, Robotics, Databases, Factual, Neural Networks, Computer, Support Vector Machine
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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