Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
This study aims to capture subtle changes in the pupil, identify relatively weak inter-class changes, extract more abstract and discriminative pupil features, and study a pupil refinement recognition method based on attention mechanisms. Based on the deep learning framework and the ResNet101 deep re...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/10971 |
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| author | Zehui Chen Changyuan Wang Gongpu Wu |
| author_facet | Zehui Chen Changyuan Wang Gongpu Wu |
| author_sort | Zehui Chen |
| collection | DOAJ |
| description | This study aims to capture subtle changes in the pupil, identify relatively weak inter-class changes, extract more abstract and discriminative pupil features, and study a pupil refinement recognition method based on attention mechanisms. Based on the deep learning framework and the ResNet101 deep residual network as the backbone network, a pupil refinement recognition model is established. Among them, the image preprocessing module is used to preprocess the pupil images captured by infrared spectroscopy, removing internal noise from the pupil images. By using the ResNet101 backbone network, subtle changes in the pupil are captured, weak inter-class changes are identified, and different features of the pupil image are extracted. The channel attention module is used to screen pupil features and obtain key pupil features. External attention modules are used to enhance the expression of key pupil feature information and extract more abstract and discriminative pupil features. The Softmax classifier is used to process the pupil features captured by infrared spectra and output refined pupil recognition results. Experimental results show that this method can effectively preprocess pupil images captured by infrared spectroscopy and extract pupil features. This method can effectively achieve fine pupil recognition, and the fine recognition effect is relatively good. |
| format | Article |
| id | doaj-art-a4e1d00be84f4ce783bf60c334bc898a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a4e1d00be84f4ce783bf60c334bc898a2025-08-20T01:55:34ZengMDPI AGApplied Sciences2076-34172024-11-0114231097110.3390/app142310971Pupil Refinement Recognition Method Based on Deep Residual Network and Attention MechanismZehui Chen0Changyuan Wang1Gongpu Wu2College of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaThis study aims to capture subtle changes in the pupil, identify relatively weak inter-class changes, extract more abstract and discriminative pupil features, and study a pupil refinement recognition method based on attention mechanisms. Based on the deep learning framework and the ResNet101 deep residual network as the backbone network, a pupil refinement recognition model is established. Among them, the image preprocessing module is used to preprocess the pupil images captured by infrared spectroscopy, removing internal noise from the pupil images. By using the ResNet101 backbone network, subtle changes in the pupil are captured, weak inter-class changes are identified, and different features of the pupil image are extracted. The channel attention module is used to screen pupil features and obtain key pupil features. External attention modules are used to enhance the expression of key pupil feature information and extract more abstract and discriminative pupil features. The Softmax classifier is used to process the pupil features captured by infrared spectra and output refined pupil recognition results. Experimental results show that this method can effectively preprocess pupil images captured by infrared spectroscopy and extract pupil features. This method can effectively achieve fine pupil recognition, and the fine recognition effect is relatively good.https://www.mdpi.com/2076-3417/14/23/10971deep residual networkattention mechanismpupil recognitionrefined identificationSoftmax classifier |
| spellingShingle | Zehui Chen Changyuan Wang Gongpu Wu Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism Applied Sciences deep residual network attention mechanism pupil recognition refined identification Softmax classifier |
| title | Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism |
| title_full | Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism |
| title_fullStr | Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism |
| title_full_unstemmed | Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism |
| title_short | Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism |
| title_sort | pupil refinement recognition method based on deep residual network and attention mechanism |
| topic | deep residual network attention mechanism pupil recognition refined identification Softmax classifier |
| url | https://www.mdpi.com/2076-3417/14/23/10971 |
| work_keys_str_mv | AT zehuichen pupilrefinementrecognitionmethodbasedondeepresidualnetworkandattentionmechanism AT changyuanwang pupilrefinementrecognitionmethodbasedondeepresidualnetworkandattentionmechanism AT gongpuwu pupilrefinementrecognitionmethodbasedondeepresidualnetworkandattentionmechanism |