Adaptive Learning Gabor Filter for Finger-Vein Recognition
Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8888260/ |
| _version_ | 1818921181417308160 |
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| author | Yakun Zhang Weijun Li Liping Zhang Xin Ning Linjun Sun Yaxuan Lu |
| author_facet | Yakun Zhang Weijun Li Liping Zhang Xin Ning Linjun Sun Yaxuan Lu |
| author_sort | Yakun Zhang |
| collection | DOAJ |
| description | Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification. |
| first_indexed | 2024-12-20T01:33:34Z |
| format | Article |
| id | doaj.art-0c6eb8b8355646eb81061ff7bcdd04f6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:33:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0c6eb8b8355646eb81061ff7bcdd04f62022-12-21T19:58:04ZengIEEEIEEE Access2169-35362019-01-01715982115983010.1109/ACCESS.2019.29506988888260Adaptive Learning Gabor Filter for Finger-Vein RecognitionYakun Zhang0https://orcid.org/0000-0001-5829-1371Weijun Li1Liping Zhang2Xin Ning3Linjun Sun4Yaxuan Lu5https://orcid.org/0000-0002-9327-200XLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaLaboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaPresently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification.https://ieeexplore.ieee.org/document/8888260/Gabor filtersvein recognitionconvolutional nerual networksadaptive learning |
| spellingShingle | Yakun Zhang Weijun Li Liping Zhang Xin Ning Linjun Sun Yaxuan Lu Adaptive Learning Gabor Filter for Finger-Vein Recognition IEEE Access Gabor filters vein recognition convolutional nerual networks adaptive learning |
| title | Adaptive Learning Gabor Filter for Finger-Vein Recognition |
| title_full | Adaptive Learning Gabor Filter for Finger-Vein Recognition |
| title_fullStr | Adaptive Learning Gabor Filter for Finger-Vein Recognition |
| title_full_unstemmed | Adaptive Learning Gabor Filter for Finger-Vein Recognition |
| title_short | Adaptive Learning Gabor Filter for Finger-Vein Recognition |
| title_sort | adaptive learning gabor filter for finger vein recognition |
| topic | Gabor filters vein recognition convolutional nerual networks adaptive learning |
| url | https://ieeexplore.ieee.org/document/8888260/ |
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