Abstract：Facial expression recognition is an important branch of facial recognition in the field of computer vision. Influenced by
many factors including the diversity of facial expressions, changes in head posture and the environment, it is a big challenge to the
work of facial expression recognition. Focused on traditional convolutional neural network, owning to its large number of parame-
ters and the limited improvement in facial expression recognition accuracy compared with applying traditional machine learning al-
gorithms, we proposes an improved convolutional neural network model based on the depthwise separable convolution structure. We
make the experiment on the Fer2013 gray expression recognition dataset based on our model, the result shows that the network struc-
ture of the model is optimized, the number of model parameters is greatly reduced, and the utilization efficiency of model parame-
ters is high, while ensuring a high accuracy rate of 68.31% compared to the traditional convolutional neural network.
鞠聪 李涛. 基于深度可分离卷积结构的人脸表情识别研究[J]. 电脑与电信, .
JU Cong LI Tao. Research on Facial Expression Recognition Based on the Depthwise
Separable Convolution Structure. Computer & Telecommunication, 2020, 1(6): 1-5.
