Face expression recognition based on NGO-BILSTM model
Introduction Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's per...
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crfrontiers:10.3389/fnbot.2023.1155038 2024-06-23T07:55:32+00:00 Face expression recognition based on NGO-BILSTM model Zhong, Jiarui Chen, Tangxian Yi, Liuhan 2023 http://dx.doi.org/10.3389/fnbot.2023.1155038 https://www.frontiersin.org/articles/10.3389/fnbot.2023.1155038/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Neurorobotics volume 17 ISSN 1662-5218 journal-article 2023 crfrontiers https://doi.org/10.3389/fnbot.2023.1155038 2024-06-11T04:09:05Z Introduction Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization. Methods In this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender. Results The results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively. Discussion In conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition. Article in Journal/Newspaper Northern Goshawk Frontiers (Publisher) Frontiers in Neurorobotics 17 |
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Introduction Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization. Methods In this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender. Results The results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively. Discussion In conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition. |
format |
Article in Journal/Newspaper |
author |
Zhong, Jiarui Chen, Tangxian Yi, Liuhan |
spellingShingle |
Zhong, Jiarui Chen, Tangxian Yi, Liuhan Face expression recognition based on NGO-BILSTM model |
author_facet |
Zhong, Jiarui Chen, Tangxian Yi, Liuhan |
author_sort |
Zhong, Jiarui |
title |
Face expression recognition based on NGO-BILSTM model |
title_short |
Face expression recognition based on NGO-BILSTM model |
title_full |
Face expression recognition based on NGO-BILSTM model |
title_fullStr |
Face expression recognition based on NGO-BILSTM model |
title_full_unstemmed |
Face expression recognition based on NGO-BILSTM model |
title_sort |
face expression recognition based on ngo-bilstm model |
publisher |
Frontiers Media SA |
publishDate |
2023 |
url |
http://dx.doi.org/10.3389/fnbot.2023.1155038 https://www.frontiersin.org/articles/10.3389/fnbot.2023.1155038/full |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Frontiers in Neurorobotics volume 17 ISSN 1662-5218 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fnbot.2023.1155038 |
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Frontiers in Neurorobotics |
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17 |
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1802648175670984704 |