Cited by 53Emotion and Mood RecognitionSocial Robot Interaction and HRIFace and Expression Recognition

Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human–Robot Interaction

Chen, Luefeng, Li, Min, Wu, Min, Pedrycz, Witold, Hirota, Kaoru·2024·IEEE Transactions on Neural Networks and Learning Systems

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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Crossref · 10.1109/tnnls.2023.3236320

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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
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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