Affiliations: [a] School of Geosciences, Yangtze University, Wuhan, Hubei, China | [b] State Key Laboratory of Geo-Information Engineering, Xi’an, Shaanxi,
China
Correspondence:
[*]
Correspondence: Yongxiang Cai, School of Geosciences, Yangtze University, Wuhan, Hubei, China. Tel.: +86 18086073069; E-mail: caiyx2002cn@126.com.
Abstract: The goal of research in Facial Expression Recognition (FER) is to build a robust and strong recognizability model. In this paper, we propose a new scheme for FER systems based on convolutional neural network. Part of the regular convolution operation is replaced by depthwise separable convolution to reduce the number of parameters and the computational workload; the self-adaption joint loss function is adopted to improve the classification performance. In addition, we balance our train set through data augmentation, and we preprocess the input images through illumination processing, face detection, and other methods, effectively maximizing the expression recognition rate. Experiments to validate our methods are conducted based on the TensorFlow platform and Fer2013 dataset. We analyze the experimental results before and after train set balancing and network model modification, and we compare our results with those of other researchers. The results show that our method is effective at increasing the expression recognition rate under the same experiment conditions. We further conduct an experiment on our own expression dataset relevant to driving safety, and it yields similar results.
Keywords: Convolutional neural network, depthwise separable convolution, facial expression recognition, self-adaption joint loss function
