%0 Journal Article
%A 王广宇
%A 王宪伦
%T Facial expression recognition based on improved ResNet
%D 2023
%R 10.19682/j.cnki.1005-8885.2023.2003
%J 中国邮电高校学报（英文）
%P 28-38
%V 30
%N 1
%X Facial expression recognition (FER) is a vital application of image processing technology. In this paper, a FER model based on the residual network is proposed. The proposed model introduces the idea of the DenseNet, in which the outputs of the residual blocks are not simply added but are linked to the channel dimension. In addition, transfer learning is used to reduce training costs and accelerate training speed. The accuracy and robustness of the proposed FER model were tested by K-fold cross-validation. Experimental results show that the proposed method has competitive performances on FER2013, FER plus (FERPlus), and the real-world affective faces database (RAF-DB).<br>
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2023.2003