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

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.