Lip Print Recognition Algorithm Based on Convolutional Network
Identity information security is faced with various challenges, and the traditional identification technology cannot meet the needs of public security. Therefore, it is necessary to further explore and study new identification technologies. In order to solve the complex image preprocessing problems,...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/4448861 |
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| _version_ | 1849471718600474624 |
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| author | Hongcheng Zhou |
| author_facet | Hongcheng Zhou |
| author_sort | Hongcheng Zhou |
| collection | DOAJ |
| description | Identity information security is faced with various challenges, and the traditional identification technology cannot meet the needs of public security. Therefore, it is necessary to further explore and study new identification technologies. In order to solve the complex image preprocessing problems, difficult feature extraction by artificial design algorithm, and low accuracy of lip print recognition, a method based on the convolutional neural network is proposed, by building a convolutional neural network called LPRNet (Lip Print Recognition Network). The obtained lip print image is inputted into the training recognition model of the network to simplify the lip print image preprocessing. By extracting feature information and sampling operation, the model training parameters are reduced, which overcomes the difficulty of designing a complex algorithm to extract features. By analyzing and comparing the experimental results, a higher recognition rate is obtained, and the validity of the method is verified. |
| format | Article |
| id | doaj-art-b987b3d349e44d60803c3e4a8dbe17fa |
| institution | Kabale University |
| issn | 1687-0042 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-b987b3d349e44d60803c3e4a8dbe17fa2025-08-20T03:24:44ZengWileyJournal of Applied Mathematics1687-00422023-01-01202310.1155/2023/4448861Lip Print Recognition Algorithm Based on Convolutional NetworkHongcheng Zhou0School of Electronic and Information EngineeringIdentity information security is faced with various challenges, and the traditional identification technology cannot meet the needs of public security. Therefore, it is necessary to further explore and study new identification technologies. In order to solve the complex image preprocessing problems, difficult feature extraction by artificial design algorithm, and low accuracy of lip print recognition, a method based on the convolutional neural network is proposed, by building a convolutional neural network called LPRNet (Lip Print Recognition Network). The obtained lip print image is inputted into the training recognition model of the network to simplify the lip print image preprocessing. By extracting feature information and sampling operation, the model training parameters are reduced, which overcomes the difficulty of designing a complex algorithm to extract features. By analyzing and comparing the experimental results, a higher recognition rate is obtained, and the validity of the method is verified.http://dx.doi.org/10.1155/2023/4448861 |
| spellingShingle | Hongcheng Zhou Lip Print Recognition Algorithm Based on Convolutional Network Journal of Applied Mathematics |
| title | Lip Print Recognition Algorithm Based on Convolutional Network |
| title_full | Lip Print Recognition Algorithm Based on Convolutional Network |
| title_fullStr | Lip Print Recognition Algorithm Based on Convolutional Network |
| title_full_unstemmed | Lip Print Recognition Algorithm Based on Convolutional Network |
| title_short | Lip Print Recognition Algorithm Based on Convolutional Network |
| title_sort | lip print recognition algorithm based on convolutional network |
| url | http://dx.doi.org/10.1155/2023/4448861 |
| work_keys_str_mv | AT hongchengzhou lipprintrecognitionalgorithmbasedonconvolutionalnetwork |