Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (4): 115-121.DOI: 10.3778/j.issn.1002-8331.1904-0309

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Data Augmentation Method Based on Generative Adversarial Networks for Facial Expression Recognition Sets

SUN Xiao, DING Xiaolong   

  1. 1.Institute of Emotional Computing and System Architecture, Hefei University of Technology, Hefei 230601, China
    2.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
  • Online:2020-02-15 Published:2020-03-06



  1. 1.合肥工业大学 情感计算与系统结构研究所,合肥 230601
    2.合肥工业大学 计算机与信息学院,合肥 230601


Deep learning methods have significantly advanced in facial expression recognition. But, facial expression databases usually do not have enough data. To solve this problem, this paper proposes a static image data augmentation method. A multi-domain image-to-image translation model based on StarGAN is implemented by modifying the reconstruction loss, which can generate multi-expression facial images from the one of a certain expression. Experiments on CK+ expression database show that this method can improve the accuracy and generalization capacity of recognition models, and can be used for reference to solve the problem of data imbalance.

Key words: data augmentation, generative adversarial networks, facial expression recognition, deep learning



关键词: 数据增强, 生成对抗网络, 人脸表情识别, 深度学习