Face expression recognition based on NGO-BILSTM model
IntroductionFacial 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 perf...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1155038/full |
| _version_ | 1797866918508494848 |
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| author | Jiarui Zhong Tangxian Chen Liuhan Yi |
| author_facet | Jiarui Zhong Tangxian Chen Liuhan Yi |
| author_sort | Jiarui Zhong |
| collection | DOAJ |
| description | IntroductionFacial 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.MethodsIn 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.ResultsThe 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.DiscussionIn 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. |
| first_indexed | 2024-04-09T23:31:59Z |
| format | Article |
| id | doaj.art-8b35182618904203ba8ae8d992f87fd4 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-09T23:31:59Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-8b35182618904203ba8ae8d992f87fd42023-03-21T04:38:27ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-03-011710.3389/fnbot.2023.11550381155038Face expression recognition based on NGO-BILSTM modelJiarui ZhongTangxian ChenLiuhan YiIntroductionFacial 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.MethodsIn 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.ResultsThe 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.DiscussionIn 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.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1155038/fullnorthern goshawk algorithmNGO-BILSTM modelface recognitionfacial expressionhyperparameter optimization |
| spellingShingle | Jiarui Zhong Tangxian Chen Liuhan Yi Face expression recognition based on NGO-BILSTM model Frontiers in Neurorobotics northern goshawk algorithm NGO-BILSTM model face recognition facial expression hyperparameter optimization |
| title | 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_short | Face expression recognition based on NGO-BILSTM model |
| title_sort | face expression recognition based on ngo bilstm model |
| topic | northern goshawk algorithm NGO-BILSTM model face recognition facial expression hyperparameter optimization |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1155038/full |
| work_keys_str_mv | AT jiaruizhong faceexpressionrecognitionbasedonngobilstmmodel AT tangxianchen faceexpressionrecognitionbasedonngobilstmmodel AT liuhanyi faceexpressionrecognitionbasedonngobilstmmodel |