The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition
Abstract
Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this article, we compare the recognition performance and robustness of two multimodal emotion recognition models: 1) deep canonical correlation analysis (DCCA) and 2) bimodal deep autoencoder (BDAE). The contributions of this article are threefold: 1) we propose two methods for extending the original DCCA model for multimodal fusion: a) weighted sum fusion and b) attention-based fusion; 2) we systemically compare the performance of DCCA, BDAE, and traditional approaches on five multimodal data sets; and 3) we investigate the robustness of DCCA, BDAE, and traditional approaches on SEED-V and DREAMER data sets under two conditions: 1) adding noises to multimodal features and 2) replacing electroencephalography features with noises. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition results on all five data sets: 1) 94.6% on the SEED data set; 2) 87.5% on the SEED-IV data set; 3) 84.3% and 85.6% on the DEAP data set; 4) 85.3% on the SEED-V data set; and 5) 89.0%, 90.6%, and 90.7% on the DREAMER data set. Meanwhile, DCCA has greater robustness when adding various amounts of noises to the SEED-V and DREAMER data sets. By visualizing features before and after DCCA transformation on the SEED-V data set, we find that the transformed features are more homogeneous and discriminative across emotions.
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Cites
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Multimodal Machine Learning: A Survey and Taxonomy
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Self-citationEnsemble Methods: Foundations and Algorithms
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AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
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Cited by
Emotion recognition in EEG signals using deep learning methods: A review
Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240
A Multi-Domain Adaptive Graph Convolutional Network for EEG-based Emotion Recognition
Li, R., Wang, Y., & Lu, B.-L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475697 · Cited by 73
Self-citationJadhav, V., Tiwari, N., & Chawla, M. (2023) · ITM Web of Conferences · doi:10.1051/itmconf/20235302011 · Cited by 11
State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy
Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11
Metadata
- Authors
- Liu, W., Qiu, J.-L., Zheng, W.-L., & Lu, B.-L.
- Venue
- IEEE Transactions on Cognitive and Developmental Systems
- Published
- 2022-06
- Volume
- 14
- Issue
- 2
- Pages
- 715-729
- ISSN
- 2379-8920 (ISSN-L)
- 2379-8939
- OpenAlex
- W3159301005
- Cited by
- 452
- References
- 60
- FWCI
- 79.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, W., Qiu, J.-L., Zheng, W.-L., & Lu, B.-L. (2022). Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/tcds.2021.3071170
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