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

<p>This study introduces a technique for facial recognition according to capsule networks. The system utilizes the advantages of capsule networks to model the face features in the image hierarchically, and realizes the efficient recognition of faces. First of all, we know the difference between the capsule network and the convolutional neural network through the study of the operating principle and the structure of the capsule network. Secondly, the Capsule Network is realized through deep research on the algorithm for dynamic routing and the internal operating principle of the capsule. Finally, by conducting experiments on the face dataset and optimizing it with the Adam optimization algorithm as well as the boundary loss and reconstruction loss, the capsule network is promoted to learn more robust feature representations to obtain better face recognition results. The experiments show that the face recognition system based on capsule network can reach 93.5% correct rate of evaluation on WebFace dataset, which achieves a high recognition accuracy. The final results demonstrate the feasibility and effectiveness of capsule networks for face recognition.</p>
</abstract>

This study introduces a technique for facial recognition according to capsule networks. The system utilizes the advantages of capsule networks to model the face features in the image hierarchically, and realizes the efficient recognition of faces. First of all, we know the difference between the capsule network and the convolutional neural network through the study of the operating principle and the structure of the capsule network. Secondly, the Capsule Network is realized through deep research on the algorithm for dynamic routing and the internal operating principle of the capsule. Finally, by conducting experiments on the face dataset and optimizing it with the Adam optimization algorithm as well as the boundary loss and reconstruction loss, the capsule network is promoted to learn more robust feature representations to obtain better face recognition results. The experiments show that the face recognition system based on capsule network can reach 93.5% correct rate of evaluation on WebFace dataset, which achieves a high recognition accuracy. The final results demonstrate the feasibility and effectiveness of capsule networks for face recognition.

Face Recognition System Based on Capsule Networks

International Journal of Advanced Network, Monitoring and Controls

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

{"article-title":"Face Recognition System Based on Capsule Networks"}

This study introduces a technique for facial recognition according to capsule networks. The system utilizes the advantages of capsule networks to model the...

Face Recognition System Based on Capsule Networks

Data publikacji: 28 mar 2024

Zakres stron: 22 - 31

DOI: https://doi.org/10.2478/ijanmc-2024-0003

Słowa kluczowe
Capsule Neural Network, Dynamic Routing, Face Recognition

© 2024 JiangRong Shi et al., published by Sciendo

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

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Face Recognition System Based on Capsule Networks

Data publikacji: 28 mar 2024

Zakres stron: 22 - 31

DOI: https://doi.org/10.2478/ijanmc-2024-0003

Słowa kluczoweCapsule Neural Network, Dynamic Routing, Face Recognition

© 2024 JiangRong Shi et al., published by Sciendo

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

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

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Słowa kluczowe
Capsule Neural Network, Dynamic Routing, Face Recognition