<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.</p>
</abstract>

Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.

Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition

Cybernetics and Information Technologies

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

Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks,...

Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition

Data publikacji: 23 mar 2024

Zakres stron: 171 - 189

Otrzymano: 06 lis 2023

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/cait-2024-0010

Słowa kluczowe
Convolutional neural networks, Dense connected network architectures, Channel and spatial attention mechanisms, Facial expression recognition

© 2024 Duong Thang Long, published by Sciendo

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

Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition

Data publikacji: 23 mar 2024

Zakres stron: 171 - 189

Otrzymano: 06 lis 2023

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/cait-2024-0010

Słowa kluczoweConvolutional neural networks, Dense connected network architectures, Channel and spatial attention mechanisms, Facial expression recognition

© 2024 Duong Thang Long, published by Sciendo

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

Słowa kluczowe
Convolutional neural networks, Dense connected network architectures, Channel and spatial attention mechanisms, Facial expression recognition