A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features
Abstract
:1. Introduction
- First and foremost, we present a novel feature representation method of FV images, which can be used to carry out spatial aggregation and feature refinement on the noisy vein pattern images, thus obtaining more robust vein structural information.
- Second, we develop a specific implementation of RLF, and apply for FV image processing. Compared with some commonly used feature extraction methods of FV images, our proposed RLF-based method can highlight vein patterns and suppress spurious non-boundary responses and noises, thus obtaining more smoothing vein structure images.
- Third, the implemented RLF-based feature extraction method demonstrates a fast running speed and a relatively low complexity of the algorithm. The experimental results also confirm the effectiveness of our method.
2. Related Works
2.1. Mean Curvature
2.2. Radon-Like Features
3. Proposed Method
3.1. ROI Localization
3.2. Implementation of Radon-Like Features
3.3. Template Matching
4. Experimental Analysis
4.1. Finger Vein Databases
4.2. Experimental Settings and Assessment Criteria
4.2.1. Experimental Settings
4.2.2. Assessment Criteria
- False Acceptance Rate (FAR), it is the error rate where the un-enrolled FV images are accepted as enrolled images. The related formula is shown in Equation (5).
- False Rejection Rate (FRR), it is the error rate where the enrolled FV images are rejected as un-enrolled images. The related formula is shown in Equation (6).
- Equal Error Rate (EER), it is defined as the ratio of trials in which the FAR is equal to the FRR. However, there may not exist a threshold such that FAR is exactly equal to FRR in practice, because FAR and FRR are both discrete values. In this case, we adopted an approximate calculation method for EER. Concretely, the EER is calculated as follows: First, let T be a set of threshold values, which are sampled from 0 to (since the match ratio is in the range of ) with a sampling interval of , namely . In this case, there are 5001 elements in set T. Supposing is the i-th threshold of T, with . If the match ratio is lower than the predefined threshold , the claimant will be accepted, otherwise, the claimant is rejected. Therefore, we can obtain a couple of and for each threshold . When the threshold is varied from 0 to , the corresponding will be reduced and will be increased. Finally, the EER can be obtained by calculating when is minimized.
4.3. Analysis on the Margin Parameters
4.4. Quantitative Comparison of Matching Performance
4.5. Visual Assessment of Matching Performance
- In the fourth row of Figure 12, the adopted Gabor filters [60] contained three scales (wavelength is set to 16, 17, 18) and eight orientations (from 22.5° to 180° with equal intervals), thus a total of 24 filters. The final result was obtained by taking the minimum value of all filters. However, the results seem poor, which is due to the fact that the method of the Gabor filter is sensitive to the threshold values, maybe a different threshold value would bring a better result.
- Finally, as shown in the last row of Figure 12, our proposed RLF-based method obtained more continuous vein lines, that is, some breaking points, which existed in the result of the mean curvature method, have been connected, thus obtain more complete and enhanced vein patterns.
4.6. Time Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Databases | HKPU | MMCBNU | FV-USM | ZSC-FV |
---|---|---|---|---|
Num of individuals | 156 | 100 | 123 | 1030 |
Fingers | index, middle | index, middle, ring | index, middle | index, middle, ring |
Hands | left | left, right | left, right | left, right |
Num of images per finger | 6/12 | 10 | 12 | 6 |
Sessions | 2 | 1 | 2 | 1 |
Num of finger classes | 312 | 600 | 492 | 6180 |
Total num of images | 3132 | 6000 | 5904 | 37,080 |
Image size | ||||
Scaled image size |
HKPU | MMCBNU | FV-USM | ZSC-FV | ||||
---|---|---|---|---|---|---|---|
Built-In | Extracted | Built-In | Extracted | Built-In | Extracted | Extracted | |
ROI | ROI | ROI | ROI | ROI | ROI | ROI | |
= 5, = 5 | – | – | 2.12% | – | 4.28% | – | – |
= 10, = 10 | 21.12% | 2.55% | 2.36% | 1.60% | 1.93% | 0.74% | 2.02% |
= 20, = 20 | 10.60% | 2.28% | 18.82% | 0.77% | 1.68% | 0.76% | 1.43% |
= 30, = 30 | 5.90% | 2.49% | – | 0.78% | 5.56% | 0.87% | 1.39% |
= 40, = 40 | 4.72% | 5.47% | – | 0.93% | 26.87% | 0.93% | 1.69% |
= 50, = 50 | 4.23% | – | – | – | – | – | 2.32% |
Databases | LLBP | Gabor Filter | WLD | Maximum Curvature | Mean Curvature | GaborPCA | Proposed RLF-Based |
---|---|---|---|---|---|---|---|
HKPU | 9.39% | 9.82% | 8.04% | 12.02% | 8.56% | 26.7% | 2.49% |
MMCBNU | 2.59% | 9.01% | 8.69% | 5.99% | 3.79% | 0.84% | 0.78% |
FV-USM | 6.16% | 10.76% | 9.89% | 4.32% | 4.08% | 1.14% | 0.87% |
ZSC-FV | 4.06% | 9.76% | 3.62% | 4.55% | 3.63% | 2.47% | 1.39% |
Databases | LLBP | Gabor Filter | WLD | Maximum Curvature | Mean Curvature | Proposed RLF-Based |
---|---|---|---|---|---|---|
HKPU | 10.86% | 14.14% | 15.6% | 20.87% | 14.64% | 5.47% |
MMCBNU | 4.46% | 11.29% | 17.85% | 12.81% | 8.39% | 3.3% |
FV-USM | 5.99% | 12.87% | 11.79% | 5.10% | 4.51% | 0.93% |
ZSC-FV | 4.11% | 12.28% | 4.37% | 5.93% | 4.37% | 1.69% |
Databases | Image Size | LLBP | Gabor Filter | WLD | Maximum Curvature | Mean Curvature | Proposed RLF-Based |
---|---|---|---|---|---|---|---|
HKPU | 254.6 | 72.3 | 39.7 | 231.2 | 4.4 | 104 | |
MMCBNU | 141.8 | 40 | 30.1 | 182.3 | 3.4 | 66.7 | |
FV-USM | 232.3 | 105.2 | 58 | 343.9 | 5.0 | 102.1 | |
ZSC-FV | 377.4 | 96 | 70 | 400.6 | 6.5 | 143 |
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Yao, Q.; Song, D.; Xu, X.; Zou, K. A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features. Sensors 2021, 21, 1885. https://doi.org/10.3390/s21051885
Yao Q, Song D, Xu X, Zou K. A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features. Sensors. 2021; 21(5):1885. https://doi.org/10.3390/s21051885
Chicago/Turabian StyleYao, Qiong, Dan Song, Xiang Xu, and Kun Zou. 2021. "A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features" Sensors 21, no. 5: 1885. https://doi.org/10.3390/s21051885
APA StyleYao, Q., Song, D., Xu, X., & Zou, K. (2021). A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features. Sensors, 21(5), 1885. https://doi.org/10.3390/s21051885