Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction
Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large con...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/9326695 |
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| _version_ | 1832558879377981440 |
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| author | Shengbin Wu |
| author_facet | Shengbin Wu |
| author_sort | Shengbin Wu |
| collection | DOAJ |
| description | Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions. |
| format | Article |
| id | doaj-art-9565ec00f50d42dfb178a6f14c9c03f3 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-9565ec00f50d42dfb178a6f14c9c03f32025-02-03T01:31:27ZengWileyJournal of Robotics1687-96192021-01-01202110.1155/2021/9326695Expression Recognition Method Using Improved VGG16 Network Model in Robot InteractionShengbin Wu0School of Information EngineeringAiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.http://dx.doi.org/10.1155/2021/9326695 |
| spellingShingle | Shengbin Wu Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction Journal of Robotics |
| title | Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction |
| title_full | Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction |
| title_fullStr | Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction |
| title_full_unstemmed | Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction |
| title_short | Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction |
| title_sort | expression recognition method using improved vgg16 network model in robot interaction |
| url | http://dx.doi.org/10.1155/2021/9326695 |
| work_keys_str_mv | AT shengbinwu expressionrecognitionmethodusingimprovedvgg16networkmodelinrobotinteraction |