Survey of Application of Deep Learning in Finger Vein Recognition
Finger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition technology based on deep neural network has made rema...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-11-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2303099.pdf |
| _version_ | 1797633187415851008 |
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| author | LI Jie, QU Zhong |
| author_facet | LI Jie, QU Zhong |
| author_sort | LI Jie, QU Zhong |
| collection | DOAJ |
| description | Finger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition technology based on deep neural network has made remarkable achievements. This paper firstly introduces the common public datasets in the field of finger vein recognition, and then classifies the applications of deep learning methods in finger vein recognition in recent years according to different neural network learning tasks, and analyzes the technical characteristics and application scenarios of each type. This paper also introduces the design techniques of deep learning in finger vein recognition from the aspects of lightweight network, data augmentation, attention mechanism and so on, and then expounds the common loss function in the model from two aspects of classifying loss and measuring learning loss. Finally, the evaluation indices of finger vein recognition system are introduced and the results of some researches on accuracy and equal error rate are summarized. In addition, the challenges and potential development directions of finger vein recognition are also presented. |
| first_indexed | 2024-03-11T11:49:33Z |
| format | Article |
| id | doaj.art-250dcc09802b4577ad4a0ca661ef1df2 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-11T11:49:33Z |
| publishDate | 2023-11-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-250dcc09802b4577ad4a0ca661ef1df22023-11-09T08:18:08ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-11-0117112557257910.3778/j.issn.1673-9418.2303099Survey of Application of Deep Learning in Finger Vein RecognitionLI Jie, QU Zhong01. School of Electronic Information and Electrical Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China 2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaFinger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition technology based on deep neural network has made remarkable achievements. This paper firstly introduces the common public datasets in the field of finger vein recognition, and then classifies the applications of deep learning methods in finger vein recognition in recent years according to different neural network learning tasks, and analyzes the technical characteristics and application scenarios of each type. This paper also introduces the design techniques of deep learning in finger vein recognition from the aspects of lightweight network, data augmentation, attention mechanism and so on, and then expounds the common loss function in the model from two aspects of classifying loss and measuring learning loss. Finally, the evaluation indices of finger vein recognition system are introduced and the results of some researches on accuracy and equal error rate are summarized. In addition, the challenges and potential development directions of finger vein recognition are also presented.http://fcst.ceaj.org/fileup/1673-9418/PDF/2303099.pdffinger vein recognition; deep learning; deep neural network; convolutional neural networks (cnn) |
| spellingShingle | LI Jie, QU Zhong Survey of Application of Deep Learning in Finger Vein Recognition Jisuanji kexue yu tansuo finger vein recognition; deep learning; deep neural network; convolutional neural networks (cnn) |
| title | Survey of Application of Deep Learning in Finger Vein Recognition |
| title_full | Survey of Application of Deep Learning in Finger Vein Recognition |
| title_fullStr | Survey of Application of Deep Learning in Finger Vein Recognition |
| title_full_unstemmed | Survey of Application of Deep Learning in Finger Vein Recognition |
| title_short | Survey of Application of Deep Learning in Finger Vein Recognition |
| title_sort | survey of application of deep learning in finger vein recognition |
| topic | finger vein recognition; deep learning; deep neural network; convolutional neural networks (cnn) |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2303099.pdf |
| work_keys_str_mv | AT lijiequzhong surveyofapplicationofdeeplearninginfingerveinrecognition |