Retracted: Image Recognition Technology Based on Neural Network
Image recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify image has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on convolutional neural network is improved,...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9160917/ |
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| _version_ | 1841557099484545024 |
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| author | Jianqiu Chen |
| author_facet | Jianqiu Chen |
| author_sort | Jianqiu Chen |
| collection | DOAJ |
| description | Image recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify image has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on convolutional neural network is improved, and the feature information of the key parts of the face is used to integrate the key part features with the global features of the face image to better distinguish similar categories. Therefore, this paper designs a method to locate the key points of the face image, and optimizes the key point positioning method through multiple experiments to facilitate the extraction of the feature information of the key points. For the calculation of classification results, a multi-region test method is used. By calculating multiple regions of the image during the test, the accuracy of image recognition can be improved. The final experimental results show that the model with key point feature information has more advantages in accuracy, and the robustness of the model is improved. |
| format | Article |
| id | doaj-art-a310305597f84b9eb55d059bbad311e4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a310305597f84b9eb55d059bbad311e42025-01-07T00:00:49ZengIEEEIEEE Access2169-35362020-01-01815716115716710.1109/ACCESS.2020.30146929160917Retracted: Image Recognition Technology Based on Neural NetworkJianqiu Chen0https://orcid.org/0000-0002-4427-8376Computer Science and Engineering, UNSW Sydney, Sydney, NSW, AustraliaImage recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify image has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on convolutional neural network is improved, and the feature information of the key parts of the face is used to integrate the key part features with the global features of the face image to better distinguish similar categories. Therefore, this paper designs a method to locate the key points of the face image, and optimizes the key point positioning method through multiple experiments to facilitate the extraction of the feature information of the key points. For the calculation of classification results, a multi-region test method is used. By calculating multiple regions of the image during the test, the accuracy of image recognition can be improved. The final experimental results show that the model with key point feature information has more advantages in accuracy, and the robustness of the model is improved.https://ieeexplore.ieee.org/document/9160917/ |
| spellingShingle | Jianqiu Chen Retracted: Image Recognition Technology Based on Neural Network IEEE Access |
| title | Retracted: Image Recognition Technology Based on Neural Network |
| title_full | Retracted: Image Recognition Technology Based on Neural Network |
| title_fullStr | Retracted: Image Recognition Technology Based on Neural Network |
| title_full_unstemmed | Retracted: Image Recognition Technology Based on Neural Network |
| title_short | Retracted: Image Recognition Technology Based on Neural Network |
| title_sort | retracted image recognition technology based on neural network |
| url | https://ieeexplore.ieee.org/document/9160917/ |
| work_keys_str_mv | AT jianqiuchen retractedimagerecognitiontechnologybasedonneuralnetwork |