%0 Journal Article %A SUN Xiao %A DING Xiaolong %T Data Augmentation Method Based on Generative Adversarial Networks for Facial Expression Recognition Sets %D 2020 %R 10.3778/j.issn.1002-8331.1904-0309 %J Computer Engineering and Applications %P 115-121 %V 56 %N 4 %X

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.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1904-0309