Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
Abstract
:1. Introduction
- Development of Thepade’s sorted block truncation coding (TSBTC) and gray-level co-occurrence matrix (GLCM) iris image data as features for the first time in iris liveness detection (ILD).
- Implementation of the fusion of best TSBTC N-ary global features with GLCM local features from an iris image, for the first time in ILD.
- Performance analysis of ML classifiers and ensembles to finalize the best classifier for ILD.
- Validating the performance of the proposed ILD method across various existing benchmark datasets and techniques.
2. Related Work
3. Proposed Iris Liveness Detection Using a Feature-Level Fusion of TSBTC and GLCM
3.1. Resizing
3.2. Feature Formation and Fusion
3.2.1. GLCM
3.2.2. TSBTC
3.2.3. Fusion of TSBTC and GLCM
3.3. Classification and Iris Liveness Detection
4. Experimental Set-Up
4.1. Description of the Dataset
- Clarkson LivDet2013—Clarkson LivDet2013 dataset has around 1536 iris images [33]. This dataset is separated into training and testing sets. To acquire images, the Dalsa sensor is used. During this experiment, the training set images are used. Table 3 shows details related to the dataset, the sensors used to acquire images, and the number of images used during this experiment. Figure 5 shows samples of images from the dataset.
- Clarkson LivDet2015—Images used in this dataset are captured using Dalsa and LG sensors [34]. Images are divided into three categories: live, pattern, and printed. In total, 25 subjects are used for live images and patterns are printed; 15 subjects each are used. The whole dataset is partitioned into training and testing.
- IIITD Combined Spoofing Database—Images used in this dataset are captured using two iris sensors, Cogent and Vista [35]. The images are divided into three categories: normal, print-scan attack, and print-capture attack.
- IIITD Contact Lens—Images used in this dataset are captured using two iris sensors, Cogent dual iris sensor and Vista FA2E single iris sensor [36,37]. The images are di-vided into three categories: normal, transparent, and colored. In total, 101 subjects are used. Both left and right iris images of each subject are captured; therefore, there are 202 iris classes.
4.2. Performance Measures
5. Results
5.1. TSBTC Results
5.2. GLCM Results
5.3. Fusion of TSBTC and GLCM Results
6. Discussions
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability
Acknowledgments
Conflicts of Interest
References
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Iris Presentation Attacks | Details |
---|---|
Print attacks | The impostor offers a printed image of validated iris to the biometric sensor [4]. |
Contact lens attacks | The impostor wears contact lenses on which the pattern of the genuine iris is printed [5]. |
Video attacks | The impostor plays a video of a registered user in front of a biometric system [6]. |
Cadaver attacks | The impostor uses the eye of a dead person in front of a biometric system [7]. |
Synthetic attacks | The impostor embeds the iris region into the authentic images to make the synthesized images more realistic [8]. |
Paper | Author/Year | Feature Extraction | Attacks Identified | Datasets | Classifiers | Performances |
---|---|---|---|---|---|---|
ID | ||||||
[17] | Thepade and Chaudhari, 2021 | TSBTC and Sauvola thresholding | NA | NR | SVM, Kstar, J48, RF, RT and ensembles | Accuracy, F-measures. |
[16] | Dewan and Thepade, 2021 | TSBTC | NA | NA | NA | ARA = 63.31% |
[12] | Jusman et al., 2020 | Hough transform, GLCM | NR | CASIA-Iris | Discriminant analysis classifiers | Accuracy = 100% |
[11] | Agarwal et al., 2020 | Texture feature, GLCM | ATVs (iris) LivDet2011 (finger) IIITD CLI dataset (iris) | SVM | ACA = 96.3% | |
[14] | Agarwal et al., 2020 | Local binary hexagonal extrema pattern | Contact Print | IIITD CLI ATVS-FIr | SVM | AER = 1.8 %, |
[13] | Khuzani et al., 2020 | Shape, density, FFT, GLCM, GLDM, and wavelet | NR | CASIA-Iris-Interval | Multilayer neural network | Accuracy = 99.64% |
[26] | Kush- waha et al., 2020 | GLCM, HOG, LBP | NA | Biometric 220X6 human footprint dataset | KNN, SVM, LDA, ensembles | Accuracy = 97.9% |
[22] | Kimura et al., 2020 | CNN | Print contact | Clarkson, Warsaw, IIITD-WVU, Notre Dame | APCER = 4.18% BPCER= 0% | |
[21] | Naqvi et al., 2020 | CNN model with a lite-residual encoder–decoder network | NA | NICE-II dataset, SBVPI | CNN | Average segmentation error = 0.0061 |
[24] | Long and Zeng, 2019 | BNCNN | Synthetic, contact | CASIA-Iris-Lamp, CASIA-Iris-Syn, ND contact | BNCNN | Correct recognition rate= 100% |
[21] | Asmara et al., 2019 | GLCM, Gabor filter | CASIA v1 Iris | Navies Bayes, SVM | Accuracy = 95.24% | |
[3] | Kaur et al., 2019 | Orthogonal rotation-invariant feature set comprising of ZMs and PHTs | Print + scan, print + capture, patterned contact lenses | IIITD-CLI, IIS, Clarkson LivDet-Iris 2015, Warsaw LivDet-Iris 2015 | KNN | Accuracy = 98.49% (given different accuracy for different datasets) |
[8] | Fathy and Ali, 2018 | Wavelet packets (WPs), local binary pattern (LBP), entropy | Print + synthetic | ATVS-Fir CASIA-Iris-Syn | SVM | ACA = 99.92% recall, precision, F1 |
[18] | Thepade et al., 2018 | TSBTC, Niblack | NR | NR | SVM, RF, ensembles, Bayes net | Accuracy = 68.56% |
[15] | Thavalen- gal et al., 2016 | Pupil localization techniques with distance metrics are used for detection | Real-time datasets | Binary tree classifier | ACER = 0% | |
[19] | Hu et al., 2016 | LBP, histogram, SID | Contact lenses, print | Clarkson, Warsaw, Notre Dame, MobBIOfake | SVM | ER, Clarkson = 7.87%, Warsaw = 6.15% ND = 0.08%, MobBIOfake = 1.50% |
Database | Sensor | Image Category | No. of Images Used for the Experiment |
---|---|---|---|
Clarkson 2013 | Dalsa | Off (live) | 350 |
Pattern (contact) | 440 | ||
Clarkson 2015 | Dalsa | Live | 378 |
Pattern | 356 | ||
Printed | 1416 | ||
LG | Live | 258 | |
Pattern | 433 | ||
Printed | 844 | ||
IIITD Combined Spoofing | Cogent | Normal | 2024 |
Print-capture | 1113 | ||
Print-scan | 980 | ||
Vista | Normal | 2024 | |
Print-capture | 1092 | ||
Print-scan | 1196 | ||
IIITD Contact | Cogent | Normal | 422 |
Transparent | 1131 | ||
Textured | 1150 | ||
Vista | Normal | 1010 | |
Transparent | 1010 | ||
Textured | 1010 |
Classifiers/Ensembles of Classifiers | Accuracy | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2-ary | 3-ary | 4-ary | 5-ary | 6-ary | 7-ary | 8-ary | 9-ary | 10-ary | 11-ary | AVG | |
NB | 83.52 | 83.67 | 83.52 | 83.96 | 84.11 | 84.11 | 84.11 | 84.25 | 84.4 | 84.4 | 83.96 |
J48 | 86.15 | 87.6 | 86.58 | 88.04 | 89.5 | 86.88 | 88.92 | 90.08 | 91.69 | 88.77 | 88.38 |
SVM | 86.58 | 86.73 | 86.73 | 86.73 | 86.73 | 86.58 | 86.44 | 86.44 | 86.58 | 86.44 | 86.62 |
RF | 89.06 | 93.29 | 93.14 | 93.87 | 94.16 | 93.29 | 93.14 | 93.29 | 93.87 | 93 | 93.01 |
MLP | 86.44 | 86.58 | 86.44 | 86.29 | 86.58 | 87.9 | 87.9 | 87.9 | 87.6 | 88.33 | 87.07 |
SVM + RF + NB | 86.44 | 86.88 | 87.17 | 87.17 | 87.17 | 87.46 | 86.73 | 86.88 | 87.17 | 86.88 | 87.01 |
SVM + RF + RT | 88.77 | 93 | 92.12 | 92.41 | 92.56 | 92.12 | 91.54 | 92.12 | 92.71 | 92.56 | 91.93 |
RF + SVM + MLP | 86.58 | 86.73 | 86.88 | 86.88 | 86.73 | 87.17 | 87.17 | 87.17 | 87.31 | 87.46 | 86.96 |
J48 + RF + MLP | 87.17 | 89.5 | 88.62 | 89.94 | 90.08 | 90.08 | 90.37 | 91.39 | 92.27 | 91.25 | 89.94 |
AVG | 86.746 | 88.22 | 87.91 | 88.37 | 88.62 | 88.4 | 88.48 | 88.84 | 89.29 | 88.79 | —— |
Classifiers/Ensembles of Classifiers | Accuracy | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2-ary | 3-ary | 4-ary | 5-ary | 6-ary | 7-ary | 8-ary | 9-ary | 10-ary | 11-ary | AVG | |
NB | 64.85 | 64.71 | 64.44 | 64.57 | 64.16 | 64.16 | 64.03 | 63.89 | 63.89 | 63.89 | 64.26 |
J48 | 75.61 | 80.79 | 82.15 | 85.96 | 87.87 | 89.1 | 90.05 | 90.32 | 91.41 | 91.28 | 86.45 |
SVM | 57.08 | 58.17 | 59.4 | 61.17 | 60.49 | 60.62 | 60.89 | 61.3 | 61.44 | 61.98 | 60.25 |
Random Forest | 83.78 | 89.64 | 91.96 | 94.27 | 94.68 | 95.5 | 94.95 | 95.5 | 95.64 | 95.23 | 93.12 |
MLP | 77.11 | 78.61 | 78.47 | 74.25 | 80.24 | 76.02 | 88.82 | 89.1 | 89.23 | 91.14 | 82.3 |
SVM + RF + NB | 66.07 | 68.39 | 70.7 | 73.56 | 73.29 | 74.65 | 73.97 | 75.61 | 76.15 | 76.15 | 72.85 |
SVM + RF + RT | 83.78 | 88.14 | 90.73 | 92.23 | 92.77 | 92.5 | 93.46 | 93.73 | 91.96 | 93.86 | 91.32 |
RF + SVM + MLP | 78.74 | 79.15 | 81.6 | 76.7 | 73.43 | 74.38 | 70.02 | 75.34 | 68.8 | 71.66 | 74.98 |
J48 + RF + MLP | 82.28 | 85.83 | 87.32 | 88.96 | 90.05 | 92.09 | 91 | 92.91 | 94 | 94 | 89.84 |
AVG | 74.367 | 77.048 | 78.53 | 79.07 | 79.66 | 79.89 | 80.8 | 81.97 | 81.39 | 82.13 | —— |
Classifiers/Ensembles of Classifiers | Accuracy | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2-ary | 3-ary | 4-ary | 5-ary | 6-ary | 7-ary | 8-ary | 9-ary | 10-ary | 11-ary | AVG | |
NB | 46.41 | 45.82 | 44.32 | 43.88 | 43.73 | 44.02 | 44.47 | 63.91 | 63.82 | 64 | 50.438 |
J48 | 64.47 | 62.08 | 64.47 | 61.19 | 64.77 | 62.98 | 63.88 | 66.37 | 65.4 | 65.58 | 64.119 |
SVM | 61.04 | 61.04 | 61.04 | 61.04 | 61.04 | 61.04 | 61.04 | 64.09 | 64.09 | 64.09 | 61.955 |
Random Forest | 63.88 | 67.61 | 71.64 | 70.44 | 75.52 | 74.92 | 75.97 | 75.41 | 76.29 | 76.73 | 72.841 |
MLP | 57.61 | 62.53 | 59.7 | 59.7 | 61.49 | 61.79 | 62.38 | 65.58 | 65.23 | 66.11 | 62.212 |
SVM + RF + NB | 60.89 | 60.74 | 61.49 | 61.94 | 62.53 | 63.88 | 65.22 | 65.67 | 65.58 | 65.75 | 63.369 |
SVM + RF + RT | 61.64 | 66.71 | 71.04 | 68.35 | 71.94 | 71.04 | 74.62 | 73.22 | 73.57 | 75.68 | 70.781 |
RF + SVM + MLP | 61.04 | 62.08 | 61.79 | 61.64 | 62.08 | 61.64 | 61.94 | 66.28 | 66.19 | 66.37 | 63.087 |
J48 + RF + MLP | 61.49 | 65.82 | 68.35 | 66.86 | 68.05 | 68.5 | 68.95 | 68.48 | 68.48 | 69.71 | 67.369 |
AVG | 59.83 | 61.603 | 62.648 | 61.671 | 63.461 | 63.312 | 64.274 | 67.667 | 67.627 | 68.22 | —— |
Classifiers Ensembles of Classifiers | Accuracy | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2-ary | 3-ary | 4-ary | 5-ary | 6-ary | 7-ary | 8-ary | 9-ary | 10-ary | 11-ary | AVG | |
NB | 90.09 | 94.99 | 95.1 | 95.2 | 94.99 | 94.99 | 94.88 | 95.2 | 95.2 | 92.49 | 94.31 |
J48 | 98.08 | 98.29 | 98.61 | 98.61 | 98.08 | 99.25 | 99.04 | 98.93 | 99.14 | 98.8 | 98.68 |
SVM | 96.48 | 96.59 | 96.8 | 97.01 | 97.01 | 97.12 | 97.12 | 97.44 | 97.55 | 97.74 | 97.08 |
Random Forest | 97.97 | 98.61 | 98.82 | 99.25 | 99.14 | 99.46 | 99.36 | 99.36 | 99.25 | 99.18 | 99.04 |
MLP | 98.93 | 99.14 | 99.25 | 99.14 | 99.14 | 99.14 | 99.04 | 99.04 | 99.04 | 98.78 | 99.06 |
SVM + RF + NB | 96.27 | 96.59 | 96.91 | 97.01 | 97.01 | 97.23 | 97.23 | 97.55 | 97.65 | 98.19 | 97.16 |
SVM + RF + RT | 97.87 | 98.4 | 98.5 | 99.04 | 99.04 | 99.25 | 99.14 | 99.25 | 99.04 | 99.17 | 98.87 |
RF + SVM + MLP | 98.61 | 98.72 | 98.72 | 98.93 | 98.93 | 98.93 | 98.93 | 98.93 | 99.04 | 98.19 | 98.79 |
J48 + RF + MLP | 98.4 | 98.72 | 98.82 | 99.04 | 99.04 | 99.57 | 99.36 | 99.25 | 99.36 | 99.15 | 99.07 |
AVG | 96.96 | 97.78 | 97.94 | 98.13 | 98.04 | 98.32 | 98.23 | 98.32 | 98.36 | 97.96 | —— |
Classifiers/Ensembles of Classifiers | Accuracy in Percentage (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Clarkson 2013 | Clarkson 2015 | IIITD Contact | IIITD Combined Spoofing | |||||||||
TSBTC + GLCM | GLCM | TSBTC | TSBTC + GLM | GLCM | TSBTC | TSBTC + GLCM | GLCM | TSBTC | TSBTC + GLCM | GLCM | TSBTC | |
NB | 83.74 | 81.77 | 84.40 | 65.33 | 67.84 | 63.89 | 58.91 | 59.51 | 63.91 | 97.55 | 92.86 | 93.85 |
J48 | 91.54 | 83.23 | 90.23 | 91.34 | 73.97 | 91.35 | 69.31 | 72.94 | 65.49 | 99.04 | 96.43 | 98.97 |
SVM | 86.73 | 82.21 | 86.51 | 61.99 | 59.94 | 61.71 | 64.09 | 73.20 | 64.09 | 98.99 | 95.51 | 97.65 |
Random Forest | 93.78 | 82.79 | 93.44 | 95.57 | 82.01 | 95.44 | 78.88 | 75.36 | 76.51 | 99.57 | 96.73 | 99.22 |
MLP | 88.26 | 83.81 | 87.97 | 90.30 | 77.11 | 90.19 | 64.13 | 73.01 | 65.67 | 99.57 | 98.06 | 98.91 |
SVM + RF + NB | 86.88 | 83.38 | 87.03 | 79.43 | 64.98 | 76.15 | 69.18 | 73.64 | 65.67 | 99.20 | 96.63 | 97.92 |
SVM + RF + RT | 91.84 | 82.50 | 92.64 | 94.34 | 81.33 | 92.91 | 76.42 | 74.79 | 74.63 | 99.57 | 97.04 | 99.11 |
RF + SVM + MLP | 87.61 | 83.81 | 87.39 | 82.49 | 75.47 | 70.23 | 67.51 | 73.32 | 66.28 | 99.62 | 97.55 | 98.62 |
J48 + RF + MLP | 92.63 | 83.81 | 91.76 | 94.00 | 79.70 | 94.00 | 76.07 | 74.72 | 69.10 | 99.68 | 97.34 | 99.26 |
AVG | 89.22 | 83.03 | 89.04 | 83.86 | 73.59 | 81.76 | 69.39 | 72.28 | 67.93 | 99.20 | 96.46 | 98.16 |
Datasets | Classifiers | Accuracy in % | Precision in % | Recall in % | F-Measure in % | APECR in % | NPCER in % | ACER in % |
---|---|---|---|---|---|---|---|---|
Clarkson 2013 | Random Forest | 93.78 | 95.50 | 86.20 | 90.60 | 7.90 | 4.12 | 6.01 |
Clarkson 2015 | Random Forest | 95.57 | 96.50 | 95.50 | 96.00 | 4.72 | 3.47 | 4.09 |
IIITD_Contact | Random Forest | 78.88 | 79.30 | 79.40 | 78.60 | 21.56 | 20.28 | 20.92 |
IIITD_Spoofing | J48 + RF + MLP | 99.68 | 99.80 | 99.80 | 99.80 | 0.12 | 0.84 | 0.48 |
Author/Year | Feature Extraction | Dataset | Performance Measure | Classifiers | Results (%) |
---|---|---|---|---|---|
P. Das et al., 2021 [39] | MSU PAD1 MSU PAD2 Notre Dame PAD | Clarkson University (CU), University of Notre Dame (ND), and Warsaw University of Technology (WUT) | APCER, BPCER, ACER | SVM, RF, MLP and CNN. | ACER = 2.61 ACER = 2.18 ACER = 28.96 |
Arora et al., 2021 [40] | CNN | IIITD | Accuracy FAR | VGGNet | Acc = 97.98 |
LeNet | Acc = 89.38 | ||||
ConvNet | Acc = 98.99 | ||||
Omran and Alshemmary 2020 [41] | CNN, IRISNet | IIITD | Sensitivity, accuracy, specificity, precision recall, G mean, and F-measure | (SVM, KNN, NB, DT | Acc = 96.43 |
Zhao et al., 2019 [42] | Mask R-CNN | IIITD | Accuracy | R-CNN, CNN | Acc = 98.9 |
Wang and Kumar 2019 [43] | CNN-SDH, CNN-Joint Bayesian | PolyU bi-spectra | Accuracy | CNN, SDH | Acc = 90.71 |
Cheng et al., 2019 [44] | CNN | CASIA-Iris-L | Accuracy | Hadamard + CNN | Acc = 97.41 |
Chatterjee et al., 2019 [45] | DWT, ResNet | ATVS | Accuracy | ResNet | Acc = 92.57 |
Proposed Approach | TSBTC, GLCM, Fusion of TSBTC and GLCM | Clarkson 2013 | Accuracy, precision, recall, and F-measure | Random Forest | Acc = 93.78 |
Clarkson 2015 | Random Forest | Acc= 95.57 | |||
IIITD Contact | Random Forest | Acc = 78.88 | |||
IIITD Combined Spoofing | J48 + RF + MLP | Acc = 99.68 ACER = 0.48 |
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Khade, S.; Gite, S.; Thepade, S.D.; Pradhan, B.; Alamri, A. Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features. Sensors 2021, 21, 7408. https://doi.org/10.3390/s21217408
Khade S, Gite S, Thepade SD, Pradhan B, Alamri A. Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features. Sensors. 2021; 21(21):7408. https://doi.org/10.3390/s21217408
Chicago/Turabian StyleKhade, Smita, Shilpa Gite, Sudeep D. Thepade, Biswajeet Pradhan, and Abdullah Alamri. 2021. "Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features" Sensors 21, no. 21: 7408. https://doi.org/10.3390/s21217408
APA StyleKhade, S., Gite, S., Thepade, S. D., Pradhan, B., & Alamri, A. (2021). Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features. Sensors, 21(21), 7408. https://doi.org/10.3390/s21217408