Iris Recognition Based on Local Grey Extremum Values with CNN-based approaches

• Autorzy: Malinowski Kamil , Saeed Khalid

Identyfikatory

DOI

10.22630/MGV.2023.32.3.11

• Języki publikacji: EN

Abstrakty

EN

One of the most important steps in the operation of biometric systems based on iris recognition of the human eye is pattern comparison. However, the comparison of the recorded pattern with the pattern stored in the database of the biometric system cannot function properly without effective extraction of key features from the iris image. In the presented work, we propose an iris recognition system based on image feature extraction and extreme grey shade analysis. Harris-Laplace, RANSAC and SIFT descriptor algorithms were used to find and describe key points. In the experimental part, two methods were used to compare descriptors: the Brute Force method and the Siamese Network method. IIT Delhi Iris Database (version 1.0), MMU v2 database, UBIRIS v1, UBIRIS v2 image databases were used for the study. The proposed method utilizes a different approach when using the generalized corner extraction algorithm (Harris-Laplace algorithms) for comparing iris patterns. In addition, we prove that the use of the descriptor and the Siamese neural networks significantly improves the results obtained in the original method based on paths alone in the case of well contrasted infrared images with very low resolutions.

Słowa kluczowe

EN

biometrics; iris; grey extremum values; encoding

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2023
• Tom: Vol. 32, No. 3/4
• Strony: 205--232
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Malinowski Kamil

• Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

• https://orcid.org/0000-0001-7285-0096

autor

Saeed Khalid

• Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

• https://orcid.org/0000-0002-7741-7045

Bibliografia

• [1] Willoughby, C. E., Ponzin, D., Ferrari, S., Lobo, A., Landau, K., Omidi, Y. (2010). Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function - A review. Clinical & Experimental Ophthalmology, 38:2-11. doi:10.1111/j.1442-9071.2010.02363.x.
• [2] Das, P., Holsopple, L., Rissacher, D., Schuckers, M., Schuckers, S. (2021). Iris Recognition Performance in Children: A Longitudinal Study. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(1):138-151. doi:10.1109/TBIOM.2021.3050094.
• [3] Zhao, Z., Kumar, A. (2017). Towards more accurate iris recognition using deeply learned spatially corresponding features. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3809-3818. doi:10.1109/ICCV.2017.411.
• [4] Hajari, K., Gawande, U., Golhar, Y. (2016). Neural network approach to iris recognition in noisy environment. Procedia Computer Science, 78:675-682. doi:10.1016/j.procs.2016.02.116.
• [5] Wang, K., Kumar, A. (2019). Toward more accurate iris recognition using dilated residual features. IEEE Transactions on Information Forensics and Security, 14(12):3233-3245. doi:10.1109/TIFS.2019.2913234
• [6] Ren, M., Wang, Y., Sun, Z., Tan, T. (2020, April). Dynamic graph representation for occlusion handling in biometrics. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 07, pp. 11940-11947. doi:10.1609/aaai.v34i07.6869.
• [7] Chicco, D. (2021). Siamese Neural Networks: An Overview. In: Cartwright, H. (eds). Artificial Neural Networks. Methods in Molecular Biology, Vol. 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_3.
• [8] Rathgeb, C., Wagner, J., Busch, C. (2019). SIFT-based iris recognition revisited: prerequisites, advantages and improvements. Pattern Analysis and Applications, 22:889-906. doi:10.1007/s10044-018-0719-y.
• [9] Tareen, S. A. K., Raza, R. H. (2023, March). Potential of SIFT, SURF, KAZE, AKAZE, ORB, BRISK, AGAST, and 7 More Algorithms for Matching Extremely Variant Image Pairs. In Proceedings of the 2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1-6. IEEE. doi:10.1109/iCoMET57998.2023.10099250.
• [10] Zhao, Y., Zhai, Y., Dubois, E., Wang, S. (2016). Image matching algorithm based on SIFT using color and exposure information. Journal of Systems Engineering and Electronics, 27(3), 691-699. doi:10.1109/JSEE.2016.00072.
• [11] Liu, C., Xu, J., Wang, F. (2021). A review of key points’ detection and feature description in image registration. Scientific Programming, 1-25, 2021. doi:10.1155/2021/8509164.
• [12] Rathgeb, C., Uhl, A. (2010, June). Secure iris recognition based on local intensity variations. In Proceedings of the International Conference Image Analysis and Recognition (ICIAR), pp. 266-275. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-13775-4_27.
• [13] Lee, M. B., Kang, J. K., Yoon, H. S., Park, K. R. (2021). Enhanced iris recognition method by generative adversarial network-based image reconstruction. IEEE Access, 9:10120-10135. doi:10.1109/ACCESS.2021.3050788
• [14] Yang, K., Xu, Z., Fei, J. (2021). Dualsanet: Dual spatial attention network for iris recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 889-897. doi:10.1109/WACV48630.2021.00093.
• [15] Chen, Y., Zeng, Z., Gan, H., Zeng, Y., Wu, W. (2021). Non-segmentation frameworks for accurate and robust iris recognition. Journal of Electronic Imaging, 30(3):033002. doi:10.1117/1.JEI.30.3.033002.
• [16] Winston, J. J., Hemanth, D. J., Angelopoulou, A., Kapetanios, E. (2021). Hybrid deep convolutional neural models for iris image recognition. Multimedia Tools and Applications, 81(7):9481-9503. doi:10.1007/s11042-021-11482-y
• [17] Liu, M., Zhou, Z., Shang, P., Xu, D. (2019). Fuzzified image enhancement for deep learning in iris recognition. IEEE Transactions on Fuzzy Systems, 28(1):92-99. doi:10.1109/TFUZZ.2019.2912576.
• [18] Chen, Y., Wu, C., Wang, Y. (2020). T-center: A novel feature extraction approach towards large-scale iris recognition. IEEE Access, 8:32365-32375. doi:10.1109/ACCESS.2020.2973433.
• [19] Liu, G., Zhou, W., Tian, L., Liu, W., Liu, Y., Xu, H. (2021). An efficient and accurate iris recognition algorithm based on a novel condensed 2-ch deep convolutional neural network. Sensors, 21(11):3721. doi:10.3390/s21113721.
• [20] Ahmadi, N., Akbarizadeh, G. (2018). Hybrid robust iris recognition approach using iris image preprocessing, two-dimensional gabor features and multi-layer perceptron neural network/PSO. IET Biometrics, 7(2):153-162. doi:10.1049/iet-bmt.2017.0041.
• [21] Ahmadi, N., Nilashi, M., Samad, S., Rashid, T. A., Ahmadi, H. (2019). An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120:105701. doi:10.1016/j.optlastec.2019.105701.
• [22] Wang, Y., Zheng, H. (2021, February). An improved Iris recognition method based on wavelet packet transform. Journal of Physics: Conference Series, Vol. 1744, No. 4, p. 042239. IOP Publishing. doi:10.1088/1742-6596/1744/4/042239.
• [23] Bala, N., Vyas, R., Gupta, R., Kumar, A. (2021). Iris Recognition Using Improved Xor-Sum Code. In Proceedings of the Conference on Security and Privacy, pp. 107-117. Springer, Singapore. doi:10.1007/978-981-33-6781-4_9.
• [24] Galdi, C., Dugelay, J. L. (2017). FIRE: Fast Iris REcognition on mobile phones by combining colour and texture features. Pattern Recognition Letters, 91:44-51. doi:10.1016/j.patrec.2017.01.023.
• [25] Lv, L., Yuan, Q., Li, Z. (2019). An algorithm of Iris feature-extracting based on 2D Log-Gabor. Multimedia Tools and Applications, 78(16):22643-22666. doi:10.1007/s11042-019-7551-2.
• [26] Abbasi, M. (2019). Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics. SN Applied Sciences, 1(7):1-14. doi:10.1007/s42452-019-0777-9.
• [27] Barpanda, S. S., Sa, P. K., Marques, O., Majhi, B., Bakshi, S. (2018). Iris recognition with tunable filter bank based feature. Multimedia Tools and Applications, 77(6):7637-7674. doi:10.1007/s11042-017-4668-z.
• [28] Barpanda, S. S., Majhi, B., Sa, P. K., Sangaiah, A. K., Bakshi, S. (2019). Iris feature extraction through wavelet mel-frequency cepstrum coefficients. Optics & Laser Technology, 110:13-23. doi:10.1016/j.optlastec.2018.03.002.
• [29] Gad, R., Talha, M., Abd El-Latif, A. A., Zorkany, M., Ayman, E. S., Nawal, E. F., Muhammad, G. (2018). Iris recognition using multi-algorithmic approaches for cognitive internet of things (CIoT) framework. Future Generation Computer Systems, 89:178-191. doi:10.1016/j.future.2018.06.020.
• [30] Liping, Y., Zhongliang, P. (2019). Iris recognition method based on Harr wavelet and Log-Gabor transform [J]. Application of Electronic Technique, 45(4):113-117. doi:10.16157/j.issn.0258-7998.183173.
• [31] Kumar, A. and Passi, A (2010). Comparison and combination of iris matchers for reliable personal authentication, Pattern Recognition, 43(3):1016-1026. doi:10.1016/j.patcog.2009.08.016.
• [32] MMU v2 MMU Iris Database, Malaysia Multimedia University, http://andyzeng.github.io/downloads/MMU2IrisDatabase.zip. [Dataset, accessed 1 November 2021].
• [33] Proença, H. and Alexandre, L. A., 2005, September. UBIRIS: A noisy iris image database. In Proceedings of the International Conference on Image Analysis and Processing (ICIAP), pp. 970-977. Springer, Berlin, Heidelberg. doi:10.1007/11553595_119.
• [34] [dataset] Proença, H., Filipe, S., Santos, R., Oliveira, J. and Alexandre, L. A. (2010). 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, 12(8):1529-1535. doi:10.1109/TPAMI.2009.66.
• [35] Malinowski, K., Saeed, K. (2022). An Efficient Algorithm for Boundary Detection of Noisy or Distorted Eye Pupil. In Proceedings of the Conference on Advanced Computing and Systems for Security, Vol. 13, pp. 51-59. Springer, Singapore. doi:10.1007/978-981-16-4287-6_4.
• [36] Malinowski, K., Saeed, K. (2022). An iris segmentation using harmony search algorithm and fast circle fitting with blob detection. Biocybernetics and Biomedical Engineering, 42(1):391-403. doi:10.1016/j.bbe.2022.02.010.
• [37] Moslhi, O. M. (2020). New full Iris Recognition System and Iris Segmentation Technique Using Image Processing and Deep Convolutional Neural Network. International Journal of Scientific Research in Multidisciplinary Studies, 6(3):20-27. https://www.isroset.org/journal/IJSRMS/full_paper_view.php?paper_id=1775
• [38] Malgheet, J. R., Manshor, N. B., Affendey, L. S., Abdul Halin, A. B. (2021). Iris Recognition Development Techniques: A Comprehensive Review. Complexity, 2021. doi:10.1155/2021/6641247.
• [39] Alvarez-Betancourt, Y., Garcia-Silvente, M. (2016). A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowledge-Based Systems, 92:169-182. doi:10.1016/j.knosys.2015.10.024
• [40] Tuytelaars, T., Mikolajczyk, K. (2008). Local invariant feature detectors: A survey. Now Publishers Inc. doi:10.1561/9781601981394.
• [41] Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110. doi:10.1023/B:VISI.0000029664.99615.94.
• [42] Noble, F. K. (2016, November). Comparison of OpenCV’s feature detectors and feature matchers. In Proceedings of the 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1-6. IEEE. doi:10.1109/M2VIP.2016.7827292.
• [43] Chopra, S., Hadsell, R., LeCun, Y. (2005, June). Learning a similarity metric discriminatively, with application to face verification. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1, pp. 539-546. IEEE. doi:10.1109/CVPR.2005.202.
• [44] Schuetzke, J., Benedix, A., Mikut, R., Reischl, M. (2020, November). Siamese Networks for 1D Signal Identification. In Proceedings of the 30. Workshop on Computational Intelligence, Berlin, 26-27 November, 2020. Vol. 26, p. 17. KIT Scientific Publishing. doi:10.5445/KSP/1000124139.
• [45] .NET Foundation and contributors. BenchmarkDotNet version 0.13.1, Copyright (c) 2013-2021, https://benchmarkdotnet.org/.
• [46] De Marsico, M., Nappi, M., Riccio, D., Wechsler, H. (2015). Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognition Letters, 57:17-23. doi:10.1016/j.patrec.2015.02.009.
• [47] Hosseini, M. S., Araabi, B. N., Soltanian-Zadeh, H. (2010). Pigment melanin: Pattern for iris recognition. IEEE Transactions on Instrumentation and Measurement, 59(4):792-804. doi:10.1109/TIM.2009.2037996.