<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Facial expressions can reflect people’s inner emotions to a certain extent, and studying facial expressions can help psychologists capture expression information in time and understand patients’ psychological changes quickly. In this paper, we establish a multi-channel convolutional neural network face expression recognition model based on the fusion of the attention mechanism. With the help of the attention mechanism and multi-channel convolutional neural network, we input expression images and perform average pooling and maximum pooling, output the features with high recognition after pooling, and identify the features with high recognition in expression images throughout the process. And with the help of multi-scale feature fusion, we improve the detection of subtle changes, such as the corners of the mouth and the eyes of the expression image target. The loss function is used to calculate the loss rate of facial expression images, which leads to the correct rate of facial expression recognition by a multi-channel convolutional neural network based on the fusion of attention mechanisms. It is demonstrated that the highest recognition correct rate of the multi-channel convolutional neural network faces expression recognition model with attention mechanism fusion is 93.56% on the FER2013 dataset, which is higher than that of the MHBP model by 23.2%. The highest correct recognition rate on the RAF-DB dataset is 91.34%, which is higher than the SR-VGG19 model by 19.39%. This shows that the multi-channel convolutional neural network face expression recognition based on the fusion of attention mechanisms improves the correct rate of facial expression recognition, which is beneficial to the research and development of psychology.</p>
</abstract>

Facial expressions can reflect people’s inner emotions to a certain extent, and studying facial expressions can help psychologists capture expression information in time and understand patients’ psychological changes quickly. In this paper, we establish a multi-channel convolutional neural network face expression recognition model based on the fusion of the attention mechanism. With the help of the attention mechanism and multi-channel convolutional neural network, we input expression images and perform average pooling and maximum pooling, output the features with high recognition after pooling, and identify the features with high recognition in expression images throughout the process. And with the help of multi-scale feature fusion, we improve the detection of subtle changes, such as the corners of the mouth and the eyes of the expression image target. The loss function is used to calculate the loss rate of facial expression images, which leads to the correct rate of facial expression recognition by a multi-channel convolutional neural network based on the fusion of attention mechanisms. It is demonstrated that the highest recognition correct rate of the multi-channel convolutional neural network faces expression recognition model with attention mechanism fusion is 93.56% on the FER2013 dataset, which is higher than that of the MHBP model by 23.2%. The highest correct recognition rate on the RAF-DB dataset is 91.34%, which is higher than the SR-VGG19 model by 19.39%. This shows that the multi-channel convolutional neural network face expression recognition based on the fusion of attention mechanisms improves the correct rate of facial expression recognition, which is beneficial to the research and development of psychology.

A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Facial expressions can reflect people’s inner emotions to a certain extent, and studying facial expressions can help psychologists capture expression...

A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 31 mar 2022

Przyjęty: 07 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00084

Słowa kluczowe
Attention mechanism, Multi-channel convolutional neural network, Multi-scale feature fusion, Face expression recognition, Loss function

© 2023 Muqing Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 31 mar 2022

Przyjęty: 07 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00084

Słowa kluczoweAttention mechanism, Multi-channel convolutional neural network, Multi-scale feature fusion, Face expression recognition, Loss function

© 2023 Muqing Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Attention mechanism, Multi-channel convolutional neural network, Multi-scale feature fusion, Face expression recognition, Loss function