Automatic emotion recognition based on expression and two-stream deep learning model

Zijian Li; Junzhe Li

doi:10.1117/12.3038795

23 August 2024 Automatic emotion recognition based on expression and two-stream deep learning model

Zijian Li, Junzhe Li

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132501K (2024) https://doi.org/10.1117/12.3038795
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In recent times, due to the fast progression of artificial intelligence technology, the recognition of emotions has emerged as a trending area of study. Among many emotion recognition technologies, the method based on expression recognition is particularly critical. We propose a two-stream model that can simultaneously extract static and dynamic expression features. To verify the information representation ability of the two-stream model for expression images, we conducted emotion classification experiments based on a deep convolutional neural network model on two public dynamic expression databases (CK+ and Oulu-CASIA). Experimental results show that our two-stream model has significant advantages in classification performance compared to single static or dynamic expression classification methods, as well as classification techniques based on 3D convolutional neural networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zijian Li and Junzhe Li "Automatic emotion recognition based on expression and two-stream deep learning model", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132501K (23 August 2024); https://doi.org/10.1117/12.3038795

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