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作者:

Lu, Shengfu (Lu, Shengfu.) | Yang, Chuang (Yang, Chuang.) | Li, Mi (Li, Mi.) | Kang, Jiaming (Kang, Jiaming.)

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EI Scopus

摘要:

Facial expression recognition has always been a focus of study in emotion computing. With the aim of solving the problem that previous facial expression recognition studies are susceptible to individual differences and background noise, as well as the loss of facial expression features in deep networks of deep convolutional networks, we propose a facial expression method based on convolutional difference contextual feature fusion. The convolutional difference module can carry out difference operation between emotional image and neutral image in the input image, eliminate individual difference and background noise in expression features and improve the accuracy and generalization of the network. Finally, convolutional difference and contextual features are combined to improve the accuracy of the network further. Experiment results of 4 public facial expression datasets illustrate that compared with the existing facial expression recognition methods, this method has higher accuracy, can extract facial expression features more effectively, and has a better generalization ability. © 2023 SPIE.

关键词:

Emotion Recognition Convolutional neural networks Convolution Face recognition Image enhancement Deep learning

作者机构:

  • [ 1 ] [Lu, Shengfu]Faculty of Information Technology, Beijing University of Technology, Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, China
  • [ 2 ] [Yang, Chuang]Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li, Mi]Faculty of Information Technology, Beijing University of Technology, Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, China
  • [ 4 ] [Kang, Jiaming]Department of Automation, Faculty of Information Technology, Beijing, China

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ISSN: 0277-786X

年份: 2023

卷: 12602

语种: 英文

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