Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds
The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/4924184 |
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| _version_ | 1832559572511883264 |
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| author | Fen Dai Ziyang Wang Xiangqun Zou Rongwen Zhang Xiaoling Deng |
| author_facet | Fen Dai Ziyang Wang Xiangqun Zou Rongwen Zhang Xiaoling Deng |
| author_sort | Fen Dai |
| collection | DOAJ |
| description | The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing with hand gesture irregularities, translation, scaling, and rotation in complex backgrounds. To reduce the computation time and model size for ultimate deploying in low-cost embedded systems, this improved HRnet was designed to be lightweight by reconstructing the residual block structure and adopting depth-separable convolution, which greatly reduced the model size and improved the inference speed of network forward propagation. Next, the palm vein ROI localization and palm vein recognition are processed in self-built dataset and two public datasets (CASIA and TJU-PV). The proposed improved HRnet algorithm achieved 97.36% accuracy for keypoints detection on self-built palm vein dataset and 98.23% and 98.74% accuracy for keypoints detection on two public palm vein datasets (CASIA and TJU-PV), respectively. The model size was only 0.45 M, and on a CPU with a clock speed of 3 GHz, the average running time of ROI extraction for one image was 0.029 s. Based on the keypoints and corresponding ROI extraction, the equal error rate (EER) of palm vein recognition was 0.000362%, 0.014541%, and 0.005951% and the false nonmatch rate was 0.000001%, 11.034725%, and 4.613714% (false match rate: 0.01%) in the self-built dataset, TJU-PV, and CASIA, respectively. The experimental result showed that the proposed algorithm was feasible and effective and provided a reliable experimental basis for the research of palm vein recognition technology. |
| format | Article |
| id | doaj-art-a480995ee8b14fd3a6dd2163c68179b9 |
| institution | Kabale University |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-a480995ee8b14fd3a6dd2163c68179b92025-02-03T01:29:45ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/4924184Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex BackgroundsFen Dai0Ziyang Wang1Xiangqun Zou2Rongwen Zhang3Xiaoling Deng4College of Electronic Engineering (College of Artifificial Intelligence)College of Electronic Engineering (College of Artifificial Intelligence)Guangzhou Intelligence Oriented Technology Co. Ltd. No. 604College of Electronic Engineering (College of Artifificial Intelligence)College of Electronic Engineering (College of Artifificial Intelligence)The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing with hand gesture irregularities, translation, scaling, and rotation in complex backgrounds. To reduce the computation time and model size for ultimate deploying in low-cost embedded systems, this improved HRnet was designed to be lightweight by reconstructing the residual block structure and adopting depth-separable convolution, which greatly reduced the model size and improved the inference speed of network forward propagation. Next, the palm vein ROI localization and palm vein recognition are processed in self-built dataset and two public datasets (CASIA and TJU-PV). The proposed improved HRnet algorithm achieved 97.36% accuracy for keypoints detection on self-built palm vein dataset and 98.23% and 98.74% accuracy for keypoints detection on two public palm vein datasets (CASIA and TJU-PV), respectively. The model size was only 0.45 M, and on a CPU with a clock speed of 3 GHz, the average running time of ROI extraction for one image was 0.029 s. Based on the keypoints and corresponding ROI extraction, the equal error rate (EER) of palm vein recognition was 0.000362%, 0.014541%, and 0.005951% and the false nonmatch rate was 0.000001%, 11.034725%, and 4.613714% (false match rate: 0.01%) in the self-built dataset, TJU-PV, and CASIA, respectively. The experimental result showed that the proposed algorithm was feasible and effective and provided a reliable experimental basis for the research of palm vein recognition technology.http://dx.doi.org/10.1049/2024/4924184 |
| spellingShingle | Fen Dai Ziyang Wang Xiangqun Zou Rongwen Zhang Xiaoling Deng Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds IET Biometrics |
| title | Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds |
| title_full | Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds |
| title_fullStr | Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds |
| title_full_unstemmed | Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds |
| title_short | Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds |
| title_sort | noncontact palm vein roi extraction based on improved lightweight hrnet in complex backgrounds |
| url | http://dx.doi.org/10.1049/2024/4924184 |
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