Face expression recognition based on residual network and support vector machine
Shuiyuan Liu · Applied and Computational Engineering · 2023
In recent years, Facial expression recognition (FER) has become an outstanding research field, which content academia and industry alike. In order to alleviate the gradient disappearance in facial expression recognition caused by increasing network depth, this paper proposes joint residual network and support vector machine (SVM) to build a model. The algorithm uses a small convolutional kernel and a deep network structure. In addition, a multi-task cascaded convolutional network (MTCNN) is constructed for data pre-processing. Meanwhile, migration learning is introduced to overcome the shortcomings of insufficient data and prevent overfitting. Extensive experiments are conducted on the FER-2013 dataset. The model achieves a recognition rate of 98.6% in the test, meanwhile the accuracy of SVM’s category is about 1% better than that of SoftMax. The experimental results proof that the model hereby propose outperforms different networks and previous methods. It further perceives that the method hereby propose is effective in recognizing facial expressions.