A Systematic Review of Finger Vein Recognition Techniques
Abstract
:1. Introduction
2. Materials
3. Finger Vein Recognition (FVR)
3.1. Image Acquisition
3.2. Preprocessing
3.2.1. Image Quality Assessment
3.2.2. ROI Extraction
3.2.3. Normalization and Enhancement
3.3. Feature Extraction
3.3.1. Vein-Based Method
3.3.2. Local Binary-Based (LBP) Method
3.3.3. Dimensionality Reduction-Based Method
3.3.4. Minutiae Point-Based Method
3.4. Matching
4. Performance Analysis
4.1. Conventional Finger Vein Recognition Method
4.2. Traditional Machine Learning Finger Vein Recognition Methods
4.3. Finger Vein Recognition Using Deep Learning Methods
4.4. Spoofing Attack (Presentation Attack) in Finger Vein Recognition
4.5. Impact of Deep Learning in Finger Vein Recognition
5. Discussion and Future Prospects
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Biometric Technique | Level of Security | Major Advantage | Disadvantage | Cost | Sensor |
---|---|---|---|---|---|
Face | Normal | Remote capture | Lighting conditions | Low | Non-contact |
Voice | Normal | Natural and convenient | Noise | Low | Non-contact |
Fingerprint | Good | Widely applied | Skin | Low | Contact |
Iris | Excellent | High accuracy | Glasses | High | Non-contact |
Finger vein | Excellent | High security level | Disease | Low | Non-contact |
Database | Acquisition Method | No. of Subjects | No. of Images | No. of Fingers for Each Subject | No. of Images for Each Subject | Image Format | Size of Images |
---|---|---|---|---|---|---|---|
SDUMLA-FV [13] | Light transmission | 106 | 3816 | 6 (both hands middle, index, ring) | 6 | bitmap | 320 × 240 pxl |
THU-FV [17] | Light transmission | 220 | 440 | 1 | 1 | bitmap | 200 × 100 pxl |
UTFV [15] | Light transmission | 60 | 1440 | 6 (both hands middle, ring, index) | 4 | PNG, 8 Bit Gray Scale | 672 × 380 pxl |
HKPU-FV [14] | Light transmission | 156 | 6264 | 3 (left hand middle, ring, index) | 12/6 * | bitmap | 513 × 256 pxl |
MMCBNU_6000 [16] | Light transmission | 100 | 6000 | 6 (both hands middle, ring, index) | 10 | bitmap | 640 × 480 pxl |
FV-USM [18] | Light transmission | 123 | 5904 | 4 (both index fingers, both middle fingers) | 6 | bitmap | 640 × 480 pxl |
VERA [21] | Light transmission | 110 | 440 | 2 (left index and right index) | 2 | PNG | 665 × 250 pxl |
Method of Preprocessing | Method of Feature Extraction | Method of Matching | References |
---|---|---|---|
Region of interest (ROI) extraction Filters combination to remove salt-and-pepper and Gaussian noise Image segmentation and denoising Extraction of vein and normalization method | Two direction weighted (2D)2LDA | - | [101] |
ROI Detection image enhancement size normalization | ONPP-Manifold learning | Manifold distance method for recogntion | [83] |
Segmentation of vein ROI Interphalangeal joint prior | Steerable filter | Nearest neighbor (NN) method | [52] |
- | BWMB2DPCA | Nearest neighbor (NN) method | [84] |
Modified Gaussian high-pass filter | Local line binary pattern | Hamming distance | [74] |
Elimination of background Removal of noise Enhancement of finger vein image Brightness Normalization Size | Dynamic thresholding edian filter Morphological operation Vein location and direction coding | Template matching | [102] |
Gaussian high-pass filter | Binarization local binary pattern | Hamming distance | [73] |
Image gray processing ROI extraction normalization | Directional Code | Matching | [103] |
Image gray processing ROI extraction Normalization (size and gray) method | Personalized best bit map (PBBM) | Matching | [104] |
Histogram equalization Bucolic interpolation | Fractal dimension Wavelet transform | Wavelet transformation Energy feature | [105] |
ROI extraction CLAHE | Linear Kernel Entropy Component analysis (KECA) | Euclidian distance | [106] |
Anisotropic diffusion method Non-scatter transmission maps Gabor wavelet | Directional filtering method | Phase only correlation strategy | [107] |
ROI extraction Enhancement Normalization size | Local line binary pattern | PWM-LLBP | [82] |
Edge detection ROI Extraction Smoothing filter | Personalized best bit map (PBBM) | Cross-correlation matching | [108] |
ROI Extraction | (HCGR) Histogram of competitive Gabor response | Matching | [109] |
ROI Extraction Brightness normalization Minimization of the Mumford–Shah Model | Morphological dilation docal entropy thresholding Morphological filtering | Template matching | [110] |
Region of interest extraction Multiscale matched filtering Line tracking | Variational approach | Sum of square differences | [111] |
ROI Extraction Image enhancement | Fractal dimension Lacunae Gabor filter | Difference compared with threshold value | [46] |
Denoising Image enhancement | Local binary pattern | Hamming distance | [20] |
ROI extraction Binarization Thinning | MCDF | [70] | |
ROI Localization Image enhancement | Uniform optimal uniform rotation invariant LBP descriptor | Histogram intersection method | [112] |
Binarized ROI Thinned Gabor filter | Minutiae-based extraction | Euclidian distance | [24] |
Image denoising ROI localization Image enhancements | LLBP PLLBP | Histogram intersection | [76] |
ROI extractions | GLLPB | Soft power metric | [77] |
Size normalization ROI Extraction Gray normalization | CLLBP | Matching score | [78] |
Preprocessing Method | Feature Extraction Method | Matching Method | Reference |
---|---|---|---|
ROI extraction, image resize | PCA, DCA | SVM and ANFIS | [86] |
ROI extraction, image resize | PCA | ANFIS (neuro-fuzzy system) | [85] |
ROI extraction, median filter, histogram equalization | Morphological operation, maximum curvature points | MLP | [113] |
Gaussian matched filter | LBPV | Global matching, SVM | [29] |
Gabor filtering | Global thresholding, Gabor filter | SVM | [61] |
ROI extraction, image resize | Convolutional neural network | Convolutional neural network | [100] |
ROI extraction, normalization | Image contrast, gradient in spatial domain, Gabor feature, information capacity and entropy | SVR | [36] |
Normalization, filtering, resizing | Grid-based location, feature-level fusion by FFF, optimization | K-SVM | [65] |
Method | Total Images | Subject | Resolution of Image | Image Format | Performance Measure | References |
---|---|---|---|---|---|---|
ONPP-Manifold learning | 11,480 | 164 | Not reported | Not reported | EER = 0.8 | [83] |
BWMB2DPCA | 660 | Not Reported | Not reported | Not reported | Accuracy = 97.7% | [84] |
Steerable filter | 1000 | 100 | 70 × 170 pixels | Not reported | CCR = 98.8%, FAR = 1.32 | [52] |
Two directional weighted (2D)2LDA | 660 | Not Reported | 80 × 200 pixels | Not reported | Accuracy = 94.69% | [101] |
Binarization Local binary pattern Local derivative pattern | 2400 | 30 | 640 × 480 pixels | Not reported | Binarization EER = 0.38, Processing Time = 30.6 ms LBP EER = 0.21, 44.7 ms LDP EER = 0.13, 112.5 ms | [73] |
Location and direction Coding (LDC) | 440 | 220 | 90 × 40 pixels | Not reported | EER = 0.44 | [113] |
LLBP | 2040 | 51 | 192 × 64 pixels | Not reported | EER = 1.78, Processing Time = 37.5 ms | [74] |
Linear kernal entropy Component analysis (KECA) | 2040 | 204 | Not reported | Not reported | Accuracy = 98% | [106] |
Personalized best bit map (PBBM) | 1484 | 106 | 96 × 64 pixels | Not reported | EER = 0.0038 | [104] |
Fractional dimension wavelet transform | 6000 | 100 | Not reported | Not reported | EER = 0.07 | [105] |
Local directional code | 4080 | 34 | 96 × 64 pixels | 24-bit color image | LDC-00 = 0.0116 LDC-45 = 0.0102 | [103] |
Directional filtering method | 9000 | 100 | 100 × 180 pixels | 8-bit gray image | EER = 0.0462 | [116] |
Personal weight maps | 1360 | 34 | 96 × 64 pixels | Not reported | EER = 0.0056 | [82] |
Identification based on pattern created by finger vein | 3600 | 100 | 320 × 240 pixels | BMP | EER = 27.56, GAR = 100, FAR = 0 | [108] |
Histogram of competitive Gabor responses (HCGR) | 6000 | 100 | 64 × 128 pixels | BMP | EER = 0.671 | [109] |
PLLBP | 1902 | Not Reported | 48 × 128 pixels | Not reported | Accuracy = 99.21% | [76] |
GLLBP | 6000 | 100 | 64 × 128 pixels | BMP | EER = 0.61, and Processing Time = 392.1 ms | [77] |
Variational approach | 2520 | 105 | Not reported | Not reported | EER = 4.47 | [111] |
Maximum curvature | - | 200 | Not reported | Not reported | FAR = 0, FRR = 1.00 | [67] |
Spectral minutiae representation (SMR) | 5000 | 125 | Not reported | Not reported | EER = 20 | [50] |
Radom forest regression method on efficient local binary pattern | 6000 | 100 | 640 × 480 pixels | Not reported | EER = 0.35, CCR = 99.65% | [99] |
Super-pixel context feature (SPCF) | PolyU = 1872 SDUMLA = 636 | PolyU = 156 SDUMLA = 106 | 96 × 64 pixels | BMP | PolyU EER = 0.0075 SDUMLA EER = 0.0697 | [117] |
Curvature in Radon space | PolyU = 2520 NTU = 680 | PolyU = 105 NTU = 85 | 186 × 71 pixels | Not reported | PolyU EER = 0.48 NTU EER = 0.69 | [3] |
Scale-invariant feature transform | 2000 | 100 | 460 × 680 pixels | Not reported | EER = 1.086 | [118] |
CLLBP | 1872 | 156 | 96 × 64 pixels | Not reported | EER = 0.055 | [78] |
Machine Learning Approach | Total Number of Images | Subjects | Resolution of Image | Image Format | Performance Measure | Reference |
---|---|---|---|---|---|---|
SVM | 100 | 10 | 20 × 20 pixels | Not reported | Accuracy = 98.00% Processing time = 0.15 s | [86] |
ANFIS (neuro-fuzzy system) | 100 | 10 | 130 × 130 pixels | Not reported | Accuracy = 99.00% Processing time = 45 s | [85] |
SVM | Not reported | Not reported | Not reported | Not reported | Training data = 95.00% Test data = 93.00% | [113] |
SVM | 800 | 10 | Not reported | Not reported | CR of Index = 90.00% CR of Ring = 96.00% CR of Middle = 90.0% CR of Little = 79.00% Index EER = 5.6 Ring EER = 6.5 Middle EER = 8.5 Little EER = 11.9 | [29] |
SVM | PKU(V2) = 200 PKU(V4) = 160 | PKU(V2) = 20 PKU(V4) = 20 | Not reported | Not reported | PKU(V2) Accuracy = 98.75% PKU(V4) Accuracy = 95% | [61] |
SVR | 1872 | 105 | Not reported | BMP | EER = 4.88 | [36] |
Multi-SVM with FFF | 1000 | 100 | 320 × 240 pixels | BMP | Accuracy = 96.00% EER = 0.35, FAR = 5, FRR = 5 Processing time = 5.1 s | [65] |
Weighted K-nearest centroid neighbor (WKNCN) | 2040 | 204 | Not reported | Not reported | Accuracy = 99.7% | [119] |
Multi-SVM | 612 | 17 | 320 × 240 pixels | BMP | EER = 0.52 Accuracy = 94% | [51] |
SVM | FV-SDU = 3816 FVUSM = 2952 | SDU = 106 USM = 123 | SDU = 320 × 240 pixels USM = 100 × 300 pixels | BMP | FV-SDU EER = 0.0359 FV-USM EER = 0.0038 | [120] |
Feature component-based extreme learning machines (FC-ELP) | 1000 | 100 | Not reported | BMP | Correct classification rate CCR = 99.53% Computational time = 0.87 ms | [115] |
Method | Total Number of Images | Subject | Resolution of Image | Image Format | Performance Measure | Reference |
---|---|---|---|---|---|---|
Fully convolutional network (FCN) | HKPU = 2520 USM = 5904 | HKPU = 105 USM = 123 | 39 × 146 pixels 50 × 150 pixels | BMP | HKPU EER = 2.70 USM EER = 1.42 | [121] |
Patch-DNN+P-SVM | Database A = 5904 Database B = 2520 | Not reported | 640 × 480 pixels 256 × 513 pixels | BMP | High- and low-quality image accuracy on Database A = 71.01%, 73.57% High- and low-quality image accuracy on Database B = 87.08%,86.36% | [122] |
Normalization +DCNN-HM | DS1 = 5000 | Not reported | 384 × 512 pixels | BMP | DS1 EER = 0.42 Total execution time = 19.27 ms | [123] |
CNN with SDH | 6264 | 156 | Not reported | Not reported | EER = 0.0977 Reduced template Size = 250 bytes | [124] |
CNN | 4800 | 64 | 376 × 328 pixels | Not reported | Accuracys= 99.4% EER = 0.21 | [127] |
CNN | 500 | 50 | 55 × 67 pixels | Not reported | Accuracy = 100.00%, total processing time = 0.15 s | [100] |
DNN+P-SVM | Database A = 2520 Database B = 5904 | Database A = 105 Database B = 123 | 50 × 240 pixels 80 × 240 pixels | BMP | EER of high- and low-quality image on Database A = 88.99% and 88.18% EER of high- and low-quality image on Database B = 74.98% and 70.07% | [37] |
CNN (Deep learning) | Good-Quality Database = 1200 | 20 | Not reported | BMP | On Good-Quality Database EER = 0.396 | [126] |
Two channel network learning | MMCBNU_6000 = 6000 SDUMLA = 3816 | 100 106 | 640 × 480 pixels 64 × 128 pixels | BMP | EER = 0.10 EER = 0.47 | [128] |
Methods | Total Number of Images | Subject | Resolution of Image | Image Format | Performance Measure | References |
---|---|---|---|---|---|---|
FSER-DWT | Live images = 3330 Fake images = 2520 | 33 | 640 × 480 pixels | Not reported | EER = 1.476 | [132] |
MSS | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | PNG | Half Total Error Rate (HTER) on full image and cropped image = 0.00% and 1.25% | [130] |
BSIF | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | PNG | HTER on full image and cropped image = 4.00% and 2.75% | [130] |
RLBP | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | HTER on cropped image = 0.00% | [130] | |
FSBE | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | PNG | HTER on full image and cropped image = 0.00% and 20.50% | [130] |
LPQ-WLD | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | PNG | HTER on full image = 0.00% | [130] |
W-DMD | Idiap database total images including real and fake = 880 | 110 | Full image = 665 × 250 pixels, Croppped image = 565 × 150 | PNG | EER on full and cropped image = 0.08% and 1.59% | [129] |
Steerable pyramids | T-Image = 300 | 100 | 100 × 300 pixels | Not reported | Average Classification Error rate (ACER) = 2.4% | [133] |
Total variation LBP | SCUT database total images including real and fake = 7200 Idiap database total images including real and fake = 880 | 100 110 | Not reported | Not reported | APCER, BPCER and ACER on both database and also on full and cropped images = 0.00% | [12] |
Transfer learning CNN with PCA and SVM | ISPR database total images including real and fake = 7560 Idiap database total images including real and fake = 880 | 7 110 | Not reported | Not reported | APCER, BPCER and ACER on both database and also full and cropped image = 0.00% | [129] |
Transferable deep convolutional neural network | FVVPA database = 300 instance FVIPA database = 300 instance | Not reported | Not reported | Not reported | APCER = 0.4% BPCER = 0.00% | [134] |
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Shaheed, K.; Liu, H.; Yang, G.; Qureshi, I.; Gou, J.; Yin, Y. A Systematic Review of Finger Vein Recognition Techniques. Information 2018, 9, 213. https://doi.org/10.3390/info9090213
Shaheed K, Liu H, Yang G, Qureshi I, Gou J, Yin Y. A Systematic Review of Finger Vein Recognition Techniques. Information. 2018; 9(9):213. https://doi.org/10.3390/info9090213
Chicago/Turabian StyleShaheed, Kashif, Hangang Liu, Gongping Yang, Imran Qureshi, Jie Gou, and Yilong Yin. 2018. "A Systematic Review of Finger Vein Recognition Techniques" Information 9, no. 9: 213. https://doi.org/10.3390/info9090213
APA StyleShaheed, K., Liu, H., Yang, G., Qureshi, I., Gou, J., & Yin, Y. (2018). A Systematic Review of Finger Vein Recognition Techniques. Information, 9(9), 213. https://doi.org/10.3390/info9090213