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作者:

Li, Zijian (Li, Zijian.) | Li, Junzhe (Li, Junzhe.)

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摘要:

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 SPIE.

关键词:

Deep neural networks Neural network models Emotion Recognition Convolutional neural networks

作者机构:

  • [ 1 ] [Li, Zijian]Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Li, Junzhe]Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

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ISSN: 0277-786X

年份: 2024

卷: 13250

语种: 英文

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