Facial Expression Recognition Based on Regularized Semi-supervised Deep Learning

  • Taiting Liu
  • Wenyan Guo
  • Zhongbo Sun
  • Yufeng Lian
  • Shuaishi LiuEmail author
  • Keping Wu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


In the field of facial expression recognition, deep learning has attracted more and more researchers’ attention as a powerful tool. The method can effectively train and test data by using a neural network. This paper mainly uses the semi-supervised deep learning model for feature extraction and adds a regularized sparse representation model as a classifier. The combination of deep learning features and sparse representations fully exploits the advantages of deep learning in feature learning and the advantages of sparse representation in recognition. Experiments show that the features obtained by deep learning have certain subspace features, which accord with the subspace hypothesis of face recognition based on sparse representation. The method of this paper has a good recognition accuracy in facial expression recognition and has certain advantages in small sample problems.


Semi-supervised learning Regularization Facial expression recognition Deep learning 



This paper is supported by Jilin Provincial Education Department “13th five-year” Science, Technology Project (No. JJKH20170571KJ), National Natural Science Foundation of China under Grant 61873304, The Science & Technology Plan Project Changchun City under Grant No. 17SS012, and the Industrial Innovation Special Funds Project of Jilin Province under Grant No. 2018C038-2 & 2019C010.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Taiting Liu
    • 1
  • Wenyan Guo
    • 1
  • Zhongbo Sun
    • 1
  • Yufeng Lian
    • 1
  • Shuaishi Liu
    • 1
    Email author
  • Keping Wu
    • 1
  1. 1.Changchun University of TechnologyChangchunChina

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