A Comparative Analysis of Hyperparameter Effects on CNN Architectures for Facial Emotion Recognition

Benjamin Grillo, Maria Kontorinaki, Fiona Sammut

2025

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.

Paper Citation

in Harvard Style

Grillo B., Kontorinaki M. and Sammut F. (2025). A Comparative Analysis of Hyperparameter Effects on CNN Architectures for Facial Emotion Recognition. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 587-596. DOI: 10.5220/0013146900003905

in Bibtex Style

@conference{icpram25,
author={Benjamin Grillo and Maria Kontorinaki and Fiona Sammut},
title={A Comparative Analysis of Hyperparameter Effects on CNN Architectures for Facial Emotion Recognition},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={587-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013146900003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Comparative Analysis of Hyperparameter Effects on CNN Architectures for Facial Emotion Recognition
SN - 978-989-758-730-6
AU - Grillo B.
AU - Kontorinaki M.
AU - Sammut F.
PY - 2025
SP - 587
EP - 596
DO - 10.5220/0013146900003905
PB - SciTePress