Face-emotion classification guided by deep convolutional neural network

Abstract

With the rapid development of computer vision and convolutional neural networks, the task of automatic face emotion classification has become a reality. The aim of this study is to improve the underlying neural network model to achieve effective face emotion classification. By presenting a simplified network to generate the recognition model, the author enhances the underlying neural network architecture. The model, in particular, augments the underlying neural network with a convolutional layer, a maximum pooling layer, and a discard layer, and increases the number of neurons in the dense layer from 25 to 128. The convolutional layer allows for the automatic extraction of sentiment features. To decrease the parameters in the feature maps, the maximum pooling layer is applied. The experiments are constructed on the Facial Emotion Recognition 2013 dataset (FER-2013). The streamlined network model improves performance by 6% to 56.32% as compared with the basic neural network model. Numerous experiments show that the proposed streamlined network model can effectively recognize facial emotions. In addition, the author analysis the confusion matrix and finds that the model has weak feedback for aversive emotions. Future research will focus on improving the representation of unclear features such as aversive emotions to enhance model generalization.