Iris presentation attack detection: Research trends, challenges, and future directions
Abstract
Currently, interest in biometrics has increased, and personal identity verification is ubiquitous. Iris recognition techniques have recently attracted considerable attention from researchers and are considered one of the most popular topics as they are used for verification purposes. Because of the increasing use of iris recognition, many potential risks have emerged as a natural result of the increased deployment of these technologies. One of the most serious risks is the so-called presentation attack (PA). A PA is the presentation of a sample to an iris sensor to trick the biometric system into making an incorrect decision. Iris presentation attacks are used to spoof or disguise a person’s identity. Many studies have focused on iris presentation attack detection techniques, which are a subset biometric recognition. However, some gaps remain unsolved, and new challenges are rapidly emerging. Despite significant advances in the literature, the problems in iris presentation attack detection have not been adequately addressed and remain open questions. This paper provides a comprehensive overview of iris presentation attack detection from various aspects (e.g., detection techniques, attack types, datasets, and performance measurements). It also attempts to explore the main challenges that may affect presentation attack detection models in terms of important aspects. The challenges that remain to be unresolved are summarised to facilitate problem solving. This review concludes with some directions for future research to help researchers focus on important aspects of the field and try to improve what previous researchers have started. Furthermore, it is likely that this review will be used as a reference for scientists/researchers in the existing science of iris presentation attack detection.
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1. Yadav S, Chen C, Ross A. Relativistic discriminator: A one-class classifier for generalized iris presentation attack detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV); 1–5 March 2020; Snowmass, CO, USA. pp. 2624–2633.
2. Raghavendra R, Raja KB, Busch C. ContlensNet: Robust iris contact lens detection using deep convolutional neural networks. In: Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV); 24–31 March 2017; Santa Rosa, CA, USA. pp. 1160–1167.
3. Kuehlkamp A, Pinto A, Rocha A, et al. Ensemble of multi-view learning classifiers for cross-domain iris presentation attack detection. IEEE Transactions on Information Forensics and Security 2019; 14(6): 1419–1431. doi: 10.1109/TIFS.2018.2878542
4. Czajka A, Bowyer KW. Presentation attack detection for iris recognition: An assessment of the state-of-the-art. ACM Computing Surveys 2018; 51(4): 1–35. doi: 10.1145/3232849
5. Boyd A, Fang Z, Czajka A, Bowyer KW. Iris presentation attack detection: Where are we now? Pattern Recognition Letters 2020; 138: 483–489. doi: 10.1016/j.patrec.2020.08.018
6. Pravallika P, Prasad KS. SVM classification for fake biometric detection using image quality assessment: Application to iris, face and palm print. In: Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT); 26–27 August 2016; Coimbatore, India. pp. 1–6.
7. Subban R, Susitha N, Mankame DP. Efficient iris recognition using Haralick features based extraction and fuzzy particle swarm optimization. Cluster Computing 2018; 21(1): 79–90. doi: 10.1007/s10586-017-0934-0
8. Gupta M, Singh V, Agarwal A, et al. Generalized iris presentation attack detection algorithm under cross-database settings. In: Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR); 10–15 January 2021; Milan, Italy. pp. 5318–5325.
9. Wang K, Kumar A. Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recognition 2019; 86: 85–98. doi: 10.1016/j.patcog.2018.08.010
10. Choudhary M, Tiwari V, Venkanna U. Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models. Soft Computing 2020; 24(15): 11477–11491. doi: 10.1007/s00500-019-04610-2
11. Nguyen K, Fookes C, Jillela R, et al. Long range iris recognition: A survey. Pattern Recognition 2017; 72: 123–143. doi: 10.1016/j.patcog.2017.05.021
12. Sava JA. Biometric authentication and identification market revenue worldwide in 2019 and 2027. Available online: https://www.statista.com/statistics/1012215/worldwide-biometric-authentication-and-identification-market-value/ (accessed on 25 February 2022).
13. Sinha VK, Gupta AK, Mahajan M. Detecting fake iris in iris bio-metric system. Digital Investigation 2018; 25: 97–104. doi: 10.1016/j.diin.2018.03.002
14. Bok JY, Suh KH, Lee EC. Detecting fake finger-vein data using remote photoplethysmography. Electronics 2019; 8(9): 1016. doi: 10.3390/electronics8091016
15. Das P, Mcfiratht J, Fang Z, Boyd A, et al. Iris liveness detection competition (LivDet-Iris)—The 2020 edition. In: Proceedings of the 2020 IEEE International Joint Conference on Biometrics (IJCB); 28 September 2020–1 October 2020; Houston, TX, USA. pp. 1–9.
16. Chen R, Lin X, Ding T. Liveness detection for iris recognition using multispectral images. Pattern Recognition Letters 2012; 33(12): 1513–1519. doi: 10.1016/j.patrec.2012.04.002
17. Yadav S, Chen C, Ross A. Synthesizing iris images using RaSGAN with application in presentation attack detection. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 16–17 June 2019; Long Beach, CA, USA. pp. 2422–2430.
18. Gautam G, Mukhopadhyay S. Challenges, taxonomy and techniques of iris localization: A survey. Digital Signal Processing 2020; 107: 102852. doi: 10.1016/j.dsp.2020.102852
19. Meenakshi K, Maragatham G. A comprehensive survey on iris presentation attacks and detection based on generative adversarial network. In: Proceedings of the 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS); 10–11 December 2020; Chennai, India. pp. 1–9.
20. Czajka A. Is that eye dead or alive? Detecting new iris biometrics attacks. Biometric Technology Today 2021; 2021(5): 9–12. doi: 10.1016/S0969-4765(21)00060-6
21. Menon H, Mukherjee A. Iris biometrics using deep convolutional networks. In: Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 14–17 May 2018; Houston, TX, USA. pp. 1–5.
22. Hoffman S, Sharma R, Ross A. Iris + ocular: Generalized iris presentation attack detection using multiple convolutional neural networks. In: Proceedings of the 2019 International Conference on Biometrics (ICB); 4–7 June 2019; Crete, Greece. pp. 1–8.
23. Yambay D, Becker B, Kohli N, et al. LivDet iris 2017—Iris liveness detection competition 2017. In: Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB); 1–4 October 2017; Denver, CO, USA. pp. 733–741.
24. Daugman JG. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993; 15(11): 1148–1161. doi: 10.1109/34.244676
25. Fang Z, Czajka A. Open source iris recognition hardware and software with presentation attack detection. In: Proceedings of the 2020 IEEE International Joint Conference on Biometrics (IJCB); 28 September 2020–1 October 2020; Houston, TX, USA. pp. 1–8.
26. Zhao J, Masood R, Seneviratne S. A review of computer vision methods in network security. IEEE Communications Surveys & Tutorials 2021; 23(3): 1838–1878. doi: 10.1109/COMST.2021.3086475
27. Raghavendra R, Busch C. Robust scheme for iris presentation attack detection using multiscale binarized statistical image features. IEEE Transactions on Information Forensics and Security 2015; 10(4): 703–715. doi: 10.1109/TIFS.2015.2400393
28. Saranya S, Sherline SV, Maheswari M. Fake biometric detection using image quality assessment: Application to iris, fingerprint recognition. In: Proceedings of the 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM); 30–31 March 2016; Chennai, India. pp. 98–103.
29. McGrath J, Bowyer KW, Czajka A. Open source presentation attack detection baseline for iris recognition. arXiv 2018. doi: 10.48550/arXiv.1809.10172
30. Wang J, Tian Q. Contact lenses detection based on the gaussian curvature. Journal of Computers 2019; 30(2): 158–164. doi: 10.3966/199115992019043002014
31. Fang Z, Czajka A, Bowyer KW. Robust iris presentation attack detection fusing 2D and 3D information. IEEE Transactions on Information Forensics and Security 2020; 16: 510–520. doi: 10.1109/TIFS.2020.3015547
32. Raja KB, Raghavendra R, Venkatesh V, Busch C. Multi-patch deep sparse histograms for iris recognition in visible spectrum using collaborative subspace for robust verification. Pattern Recognition Letters 2017; 91: 27–36. doi: 10.1016/j.patrec.2016.12.025
33. Kaur B. Iris spoofing detection using discrete orthogonal moments. Multimedia Tools and Applications 2020; 79(9): 6623–6647. doi: 10.1007/s11042-019-08281-x
34. Shahriar H, Haddad H, Islam M. An iris-based authentication framework to prevent presentation attacks. In: Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC); 4–8 July 2017; Turin, Italy. pp. 504–509.
35. Kaur B, Singh S, Kumar J. Cross-sensor iris spoofing detection using orthogonal features. Computers & Electrical Engineering 2019; 73: 279–288. doi: 10.1016/j.compeleceng.2018.12.002
36. Malhotra A, Gupta R. Iris anti-spoofing under varying illumination conditions. In: Proceedings of the 2016 1st India International Conference on Information Processing (IICIP); 12–14 August 2016; Delhi, India. pp. 1–6.
37. Raja KB, Raghavendra R, Busch C. Presentation attack detection using Laplacian decomposed frequency response for visible spectrum and near-infra-red iris systems. In: Proceedings of the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS); 8–11 September 2015; Arlington, VA, USA. pp. 1–8.
38. Fathy WSA, Ali HS, Mahmoud II. Statistical representation for iris anti-spoofing using wavelet-based feature extraction and selection algorithms. In: Proceedings of the 2017 34th National Radio Science Conference (NRSC); 13–16 March 2017; Alexandria, Egypt. pp. 221–229.
39. Kohli N, Yadav D, Vatasa M, et al. Detecting medley of iris spoofing attacks using DESIST. In: Proceedings of the 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS); 6–9 September 2016; Niagara Falls, NY, USA. pp. 1–6.
40. Gragnaniello D, Poggi G, Sansone C, et al. An investigation of local descriptors for biometric spoofing detection. IEEE Transactions on Information Forensics and Security 2015; 10(4): 849–863. doi: 10.1109/TIFS.2015.2404294
41. Agarwal R, Jalal AS, Arya KV. Local binary hexagonal extrema pattern (LBHXEP): A new feature descriptor for fake iris detection. The Visual Computer 2021; 37(6): 1357–1368. doi: 10.1007/s00371-020-01870-0
42. Das A, Pal U, Ferrer MA, Blumenstein M. A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics. Pattern Recognition Letters 2016; 82: 232–241. doi: 10.1016/j.patrec.2015.11.016
43. Czajka A, Fang Z, Bowyer K. Iris presentation attack detection based on photometric stereo features. In: Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV); 7–11 January 2019; Waikoloa, HI, USA. pp. 877–885.
44. Yadav D, Kohli N, Agarwal A, et al. Fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 18–22 June 2018; Salt Lake City, UT, USA. pp. 685–6857.
45. Kannala J, Rahtu E. BSIF: Binarized statistical image features. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012); 11–15 November 2012; Tsukuba, Japan. pp. 1363–1366.
46. Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security 2016; 11(8): 1818–1830. doi: 10.1109/TIFS.2016.2555286
47. Yadav D, Kohli N, Yadav S, et al. Iris presentation attack via textured contact lens in unconstrained environment. In: Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV); 12–15 March 2018; Lake Tahoe, NV, USA. pp. 503–511.
48. Czajka A, Bowyer KW, Krumdick M, et al. Recognition of image-orientation-based iris spoofing. IEEE Transactions on Information Forensics and Security 2017; 12(9): 2184–2196. doi: 10.1109/TIFS.2017.2701332
49. Choudhary M, Tiwari V, Venkanna U. Iris anti-spoofing through score-level fusion of handcrafted and data-driven features. Applied Soft Computing 2020; 91: 106206. doi: 10.1016/j.asoc.2020.106206
50. Nguyen DT, Pham TD, Lee YW, Park KR. Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on NIR camera sensor. Sensors 2018; 18(8): 2601. doi: 10.3390/s18082601
51. Choudhary M, Tiwari V, Uduthalapally V. Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI. Neural Computing and Applications 2021; 33(11): 5609–5629. doi: 10.1007/s00521-020-05342-3
52. Nguyen DT, Baek NR, Pham TD, Park KR. Presentation attack detection for iris recognition system using NIR camera sensor. Sensors 2018; 18(5): 1315. doi: 10.3390/s18051315
53. Menotti D, Chiachia G, Pinto A, et al. Deep representations for iris, face, and fingerprint spoofing detection. IEEE Transactions on Information Forensics and Security 2015; 10(4): 864–879. doi: 10.1109/TIFS.2015.2398817
54. Chen C, Ross A. Exploring the use of irisCodes for presentation attack detection. In: Proceedings of the 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS); 22–25 October 2018; Redondo Beach, CA, USA. pp. 1–9.
55. Sharma R, Ross A. D-NetPAD: An explainable and interpretable iris presentation attack detector. In: Proceedings of the 2020 IEEE International Joint Conference on Biometrics (IJCB); 28 September 2020–1 October 2020; Houston, TX, USA. pp. 1–10.
56. Choudhary M, Tiwari V, Venkanna U. An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM. Future Generation Computer Systems 2019; 101: 1259–1270. doi: 10.1016/j.future.2019.07.003
57. Dhar P, Kumar A, Kaplan K, et al. EyePAD++: A distillation-based approach for joint eye authentication and presentation attack detection using periocular images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 18–24 June 2022; New Orleans, LA, USA. pp. 20186–20195.
58. Fang M, Damer N, Fadi B, et al. Deep learning multi-layer fusion for an accurate iris presentation attack detection. In: Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION); 6–9 July 2020; Rustenburg, South Africa. pp. 1–8.
59. Arora S, Bhatia MPS. Presentation attack detection for iris recognition using deep learning. International Journal of System Assurance Engineering and Management 2020; 11(2): 232–238. doi: 10.1007/s13198-020-00948-1
60. He L, Li H, Liu F, et al. Multi-patch convolution neural network for iris liveness detection. In: Proceedings of the 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS); 6–9 September 2016; Niagara Falls, NY, USA. pp. 1–7.
61. Trokielewicz M, Czajka A, Maciejewicz P. Presentation attack detection for cadaver iris. In: Proceedings of the 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS); 22–25 October 2018; Redondo Beach, CA, USA. pp. 1–10.
62. Fang M, Damer N, Boutros F, et al. Cross-database and cross-attack Iris presentation attack detection using micro stripes analyses. Image and Vision Computing 2021; 105: 104057. doi: 10.1016/j.imavis.2020.104057
63. Boyd A, Speth Jeremy, Parzanello L, et al. State of the art in open-set iris presentation attack detection. arXiv 2022. doi: 10.48550/arXiv.2208.10564
64. Fang M, Boutros F, Damer N. Intra and cross-spectrum iris presentation attack detection in the NIR and visible domains using attention-based and pixel-wise supervised learning. arXiv 2022.
65. Yadav D, Kohli N, Vatsa M, et al. Detecting textured contact lens in uncontrolled environment using DensePAD. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 16–17 June 2019; Long Beach, CA, USA. pp. 2336–2344.
66. Hoffman S, Sharma R, Ross A. Convolutional neural networks for iris presentation attack detection: Toward cross-dataset and cross-sensor generalization. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 18–22 June 2018; Salt Lake City, UT. pp. 1701–1708.
67. Chatterjee P, Roy K. Anti-spoofing approach using deep convolutional neural network. In: Mouhoub M, Sadaoui S, Ait Mohamed O (editors). Recent Trends and Future Technology in Applied Intelligence, Proceedings of the International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems; 25–28 June 2018; Montreal, QC, Canada. Springer, Cham; 2018. Volume 10868, pp. 745–750.
68. Gragnaniello D, Sansone C, Poggi G, Verdoliva L. Biometric spoofing detection by a domain-aware convolutional neural network. In: Proceedings of the 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); 28 November 2016–1 December 2016; Naples, Italy. pp. 193–198.
69. Parzianello L, Czajka A. Saliency-guided textured contact lens-aware iris recognition. In: Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW); 4–8 January 2022; Waikoloa, HI, USA. pp. 330–337.
70. Chen C, Ross A. A multi-task convolutional neural network for joint iris detection and presentation attack detection. In: Proceedings of the 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW); 15 March 2018; Lake Tahoe, NV, USA. pp. 44–51.
71. Raju MH, Lohr DJ, Komogortse O. Iris print attack detection using eye movement signals. In: Proceedings of the 2022 Symposium on Eye Tracking Research and Applications; 8–11 June 2022; Seattle, WA, USA. pp. 1–6.
72. Fang M, Damer N, Boutros F, et al. The overlapping effect and fusion protocols of data augmentation techniques in iris PAD. Machine Vision and Applications 2022; 33(1): 1–21. doi: 10.1007/s00138-021-01256-9
73. Pala F, Bhanu B. Iris liveness detection by relative distance comparisons. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 21–26 July 2017; Honolulu, HI, USA. pp. 664–671.
74. Poster D, Nasrabadi N, Riggan B. Deep sparse feature selection and fusion for textured contact lens detection. In: Proceedings of the 2018 International Conference of the Biometrics Special Interest Group (BIOSIG); 26–28 September 2018; Darmstadt, Germany. pp. 1–5.
75. Fang M, Damer N, Boutros F, et al. Iris presentation attack detection by attention-based and deep pixel-wise binary supervision network. In: 2021 IEEE International Joint Conference on Biometrics (IJCB); 4–7 August 2021; Shenzhen, China. pp. 1–8.
76. Li Y, Lian Y, Wang J, et al. Few-shot one-class domain adaptation based on frequency for iris presentation attack detection. In: Proceedings of the ICASSP 2022—2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 23–27 May 2022; Singapore, Singapore. pp. 2480–2484.
77. Luo Z, Wang Y, Liu N, Wang Z. Combining 2D texture and 3D geometry features for reliable iris presentation attack detection using light field focal stack. IET Biometrics 2022; 11(5): 420–429. doi: 10.1049/bme2.12092
78. Sequeira AF, Sliva W, Pinto JR, et al. Interpretable biometrics: Should we rethink how presentation attack detection is evaluated? In: Proceedings of the 2020 8th International Workshop on Biometrics and Forensics (IWBF); 29–30 April 2020; Porto, Portugal. pp. 1–6.
79. El‐Din YS, Moustafa MN, Mahdi H. Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study. IET Biometrics 2020; 9(5): 179–193. doi: 10.1049/iet-bmt.2020.0004
80. Galbally J, Marcel S, Fierrez J. Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE Transactions on Image Processing 2013; 23(2): 710–724. doi: 10.1109/TIP.2013.2292332
81. Choudhary M, Tiwari V, Venkanna U. Iris liveness detection using fusion of domain-specific multiple BSIF and DenseNet features. IEEE Transactions on Cybernetics 2022; 52(4): 2370–2381. doi: 10.1109/TCYB.2020.3005089
82. Khade S, Ahirrao S, Phansalkar S, et al. Iris liveness detection for biometric authentication: A systematic literature review and future directions. Inventions 2021; 6(4): 65. doi: 10.3390/inventions6040065
83. Kohli N, Yadav D, Vatsa M, et al. Synthetic iris presentation attack using iDCGAN. In: Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB); 1–4 October 2017; Denver, CO, USA. pp. 674–680.
84. Yadav S, Ross A. CIT-GAN: Cyclic image translation generative adversarial network with application in iris presentation attack detection. In: Proceedings of the 2021 IEEE/CVF Winter Conference on Applications of Computer Vision (WACA); 3–8 January 2021; Waikoloa, HI, USA. pp. 2411–2420.
85. CASIA iris database V4. Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=14#/ (accessed on 9 March 2022).
86. Gupta P, Behera S, Vatsa M, Singh R. On iris spoofing using print attack. In: Proceedings of the 2014 22nd International Conference on Pattern Recognition; 24–28 August 2014; Stockholm, Sweden. pp. 1681–1686.
87. Fierrez J, Ortega-Garcia J, Toledano DT, Gonzalez-Rodriguez J. Biosec baseline corpus: A multimodal biometric database. Pattern Recognition 2007; 40(4): 1389–1392. doi: 10.1016/j.patcog.2006.10.014
88. Doyle JS, Bowyer KW. Robust detection of textured contact lenses in iris recognition using BSIF. IEEE Access 2015; 3: 1672–1683. doi: 10.1109/ACCESS.2015.2477470
89. Doyle JS, Bowyer KW, Flynn PJ. Variation in accuracy of textured contact lens detection based on sensor and lens pattern. In: Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS); 29 September 2013–2 October 2013; Arlington, VA, USA. pp. 1–7.
90. WVU mobile iris spoofing dataset. Available online: https://iab-rubric.org/resources/biometric-datasets/iris (accessed on 1 November 2023).
91. Proença H, Filipe S, Santos R, et al. The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 2010; 32(8): 1529–1535. doi: 10.1109/TPAMI.2009.66
92. Trokielewicz M, Czajka A, Maciejewicz P. Iris recognition after death. IEEE Transactions on Information Forensics and Security 2019; 14(6): 1501–1514. doi: 10.1109/TIFS.2018.2881671
93. Mostofa M, Mohamadi S, Dawson J, Nasrabadi NM. Deep GAN-based cross-spectral cross-resolution iris recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science 2021; 3(4): 443–463. doi: 10.1109/TBIOM.2021.3102736
94. Regulation (EU) 2016/679 of the European parliament and of the council. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679 (accessed on 6 October 2022).
95. Bowyer KW, Hollingsworth K, Flynn PJ. Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding 2008; 110(2): 281–307. doi: 10.1016/j.cviu.2007.08.005
96. Fu B, Damer N. Towards explaining demographic bias through the eyes of face recognition models. In: Proceedings of the 2022 IEEE International Joint Conference on Biometrics (IJCB); 10–13 October 2022; Abu Dhabi, United Arab Emirates. pp. 1–10.
97. Fang M, Damer N, Kirchbuchner F, Kuijper A. Demographic bias in presentation attack detection of iris recognition systems. In: Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO); 18–21 January 2021; Amsterdam, Netherlands. pp. 835–839.
DOI: https://doi.org/10.32629/jai.v7i2.1012
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