DNN-CBAM: An enhanced DNN model for facial emotion recognition

Abstract

Facial expression recognition is a current research hotspot and can be applied to computer vision fields such as human-computer interaction and affective computing. The lack of diversity and category recognition information in the neural network input may affect the performance of the network, resulting in insufficient extraction of facial expression features. In order to address the above problems, a lightweight deep convolution neural network with convolution block attention module is proposed in this paper. The implementation of the lightweight DNN relies on the use of deep separable convolution and residual blocks. The combination of the convolution block attention module and the improved classification function can optimize the lightweight model. We use accuracy and confusion matrix to evaluate different models, ultimately achieving 71.5% and 99.5% accuracy on the Fer2013 and CK+ datasets respectively. The experimental results show that our model has good feature representation capabilities.