Convolutional neural network combined with the attention mechanism for facial emotion recognition

Xiguo Luo

doi:10.54254/2755-2721/22/20231157

Research Article

Open access

Published on 23 October 2023

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Convolutional neural network combined with the attention mechanism for facial emotion recognition

Xiguo Luo *^,1,

¹ Durham University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/22/20231157

Abstract

Facial Emotion Recognition (FER) holds great importance in the fields of computer vision and machine learning. In this study, the aim is to improve the accuracy of facial expression recognition by incorporating attention mechanisms into Convolutional Neural Networks (CNN) with FER2013 dataset, which consists of grayscale images categorized into seven expressions. The combination of proposed CNN architecture and attention mechanisms is thoroughly elucidated, emphasizing the operations and interactions of their components. Additionally, the effectiveness of the new model is evaluated through experiments, comparing its performance with existing approaches in terms of accuracy. Besides, the results demonstrate that the CNN architecture with attention mechanisms outperforms the original CNN by achieving an improved accuracy rate of 69.07%, which is higher than 68.04% accuracy rate of original CNN. Moreover, the study further discusses the confusion matrix analysis, revealing the challenges faced in recognizing specific emotions due to limited training data and vague facial features. In the future, this study suggests addressing these limitations through data augmentation and to reduce the gap between training and testing accuracy. Overall, this research highlights the potential of attention mechanisms in enhancing facial expression recognition systems, paving the way for advanced applications in various domains.

Keywords

convolutional neural network, deep learning, FER

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Cite this article

Luo,X. (2023). Convolutional neural network combined with the attention mechanism for facial emotion recognition. Applied and Computational Engineering,22,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.22

ISSN：2755-2721(Print) / 2755-273X(Online)

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