A comparative analysis of hyperparameter effects on CNN architectures for facial emotion recognition

Abstract

This study investigates facial emotion recognition, an area of computer vision that involves identifying human emotions from facial expressions. It approaches facial emotion recognition as a classification task using labelled images. More specifically, we use the FER2013 dataset and employ Convolutional Neural Networks due to their capacity to efficiently process and extract hierarchical features from image data. This research utilises custom network architectures to compare the impact of various hyperparameters - such as the number of convolutional layers, regularisation parameters, and learning rates - on model performance. Hyperparameters are systematically tuned to determine their effects on accuracy and overall performance. According to various studies, the best-performing models on the FER2013 dataset surpass human-level performance, which is between 65% and 68%. While our models did not achieve the best-reported accuracy in literature, the findings still provide valuable insights into hyperparameter optimisation for facial emotion recognition, demonstrating the impact of different configurations on model performance and contributing to ongoing research in this area.