Parameter optimization in convolutional neural network for improving performance of facial expression recognition

Abstract

The recognition of similar facial expressions presents a notable challenge, necessitating a focus on the parameters within the fundamental Convolutional Neural Network (CNN) architecture, which serves as a cornerstone in the field of image classification. This research endeavor aims to enhance the model's capacity for facial expression recognition by employing a controlled variable method to examine two specific parameters in a self-designed small CNN: the number of filters and convolutional layers. More specifically, while the filters were fixed at 3, the layers varied from 3 to 6 to 9. Similarly, as the number of the filters rose to 6, the number of the layers also incremented from 3 to 6 to 9. Furthermore, while the number of the filters reached 12, the number of the layers went from 3 to 6 to 9 too. Finally, with the filters increasing to 24, the layers rose from 3 to 6 to 9 as well. Experimental results indicate that both increasing the number of filters and convolutional layers can increment the performance of model in facial expression recognition. Furthermore, increasing the number of filters can exert a more prominent influence on improving the accuracy of facial expression recognition.