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Deep auto-encoders with sequential learning for multimodal dimensional emotion recognition


Nguyen, D and Nguyen, DT and Zeng, R and Nguyen, TT and Tran, S and Nguyen, TK and Sridharan, S and Fookes, C, Deep auto-encoders with sequential learning for multimodal dimensional emotion recognition, IEEE Transactions on Multimedia pp. 1-12. ISSN 1520-9210 (2021) [Refereed Article]


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DOI: doi:10.1109/TMM.2021.3063612


Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet representative features from multimodal data, (ii) how to effectively capture complementary features from multimodal streams, and (iii) how to perform all the tasks in an end-to-end manner. To address these challenges, in this paper, we propose a novel deep neural network architecture consisting of a two-stream auto-encoder and a long short term memory for effectively integrating visual and audio signal streams for emotion recognition. To validate the robustness of our proposed architecture, we carry out extensive experiments on the multimodal emotion in the wild dataset: RECOLA. Experimental results show that the proposed method achieves state-of-the-art recognition performance.

Item Details

Item Type:Refereed Article
Keywords:multimodal emotion recognition, dimensional emotion recognition, auto-encoder, long short term memory, deep learning
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Behaviour and health
UTAS Author:Tran, S (Dr Son Tran)
ID Code:146291
Year Published:2021
Web of Science® Times Cited:3
Deposited By:Information and Communication Technology
Deposited On:2021-08-27
Last Modified:2021-11-24
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