Survey of Iris Recognition using Deep Learning Techniques
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.826Keywords:
Iris Recognition, Biometric, Deep LearningAbstract
Deep learning is an effective data mining method that is used to analyze complex, and large quantities of data accurately and efficiently. In the last few years, the world has gone through an revolutionary change in the way of how data produced and how data are processed not similar to any time before. The data produced must be handled accurately using intelligent methods to get accurate results. For example, iris recognition is one of the applications that needs sophisticated algorithms capable to identify one person from the other via the iris data analysis. In the recent few year, it was clear how deep learning has been used in different areas of life. One of those areas is the pattern recognition area. In this review paper, we focus on the investigation of using the deep learning technologies for these purposes. The research methodology followed in this paper is based on reviewing, analyzing the academic papers published in the last couple of years in terms of the proposed paradigm used on the iris data, and the accuracy results obtained from using that paradigm as well as mentioning the datasets used in these paper. The outcomes of this paper showed that using the deep learning method, in particular, the Convolutional neural networks, has promising future due to its success in this domain.
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References
[2] Paliwal, Mukta, and Usha A. Kumar. "Neural networks and statistical techniques: A review of applications." Expert systems with applications 36.1 (2009): 2-17.
[3] Zhao, Zijing, and Ajay Kumar. "Towards more accurate iris recognition using deeply learned spatially corresponding features." Proceedings of the IEEE International Conference on Computer Vision. 2017.
[4] Arsalan, Muhammad, et al. "Deep learning-based iris segmentation for iris recognition in visible light environment." Symmetry 9.11 (2017): 263.
[5] Karakaya, Mahmut. "Deep Learning Frameworks for Off-Angle Iris Recognition." 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2018.
[6] Zanlorensi, Luiz A., et al. "The impact of preprocessing on deep representations for iris recognition on unconstrained environments." 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018.
[7] Varkarakis, Viktor, Shabab Bazrafkan, and Peter Corcoran. "A deep learning approach to segmentation of distorted iris regions in head-mounted displays." 2018 IEEE Games, Entertainment, Media Conference (GEM). IEEE, 2018.
[8] Bazrafkan, Shabab, and Peter Corcoran. "Enhancing iris authentication on handheld devices using deep learning derived segmentation techniques." 2018 IEEE international conference on consumer electronics (ICCE). IEEE, 2018.
[9] Minaee, Shervin, and Amirali Abdolrashidi. "Deepiris: Iris recognition using a deep learning approach." arXiv preprint arXiv:1907.09380 (2019).
[10] Li, Yung-Hui, Po-Jen Huang, and Yun Juan. "An efficient and robust iris segmentation algorithm using deep learning." Mobile Information Systems 2019 (2019).
[11] Khalifa, Nour Eldeen M., et al. "Deep iris: deep learning for gender classification through iris patterns." Acta Informatica Medica 27.2 (2019): 96.
[12] Liu, Ming, et al. "Fuzzified image enhancement for deep learning in iris recognition." IEEE Transactions on Fuzzy Systems 28.1 (2019): 92-99.
[13] Lozej, Juš, et al. "Influence of segmentation on deep iris recognition performance." 2019 7th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2019.
[14] Wang, Caiyong, et al. "Joint iris segmentation and localization using deep multi-task learning framework." arXiv preprint arXiv:1901.11195 (2019).
[15] Kerrigan, Daniel, et al. "Iris recognition with image segmentation employing retrained off-the-shelf deep neural networks." 2019 International Conference on Biometrics (ICB). IEEE, 2019.
[16] Wang, Kuo, and Ajay Kumar. "Toward more accurate iris recognition using dilated residual features." IEEE Transactions on Information Forensics and Security 14.12 (2019): 3233-3245.
[17] Sardar, Mousumi, Subhashis Banerjee, and Sushmita Mitra. "Iris Segmentation Using Interactive Deep Learning." IEEE Access 8 (2020): 219322-219330.
[18] Thakkar, Sejal, and Chirag Patel. "Iris Recognition Supported best Gabor Filters and Deep learning CNN Options." 2020 International Conference on Industry 4.0 Technology (I4Tech). IEEE, 2020.
[19] Wang, Caiyong, et al. "Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition." IEEE Transactions on information forensics and security 15 (2020): 2944-2959.
[20] Karakaya, Mahmut, et al. "Limbus impact on off-angle iris degradation." 2013 International Conference on Biometrics (ICB). IEEE, 2013.
[21] Sutra, Guillaume, Sonia Garcia-Salicetti, and Bernadette Dorizzi. "The Viterbi algorithm at different resolutions for enhanced iris segmentation." 2012 5th IAPR International Conference on Biometrics (ICB). IEEE, 2012.
[22] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.
[23] Xie, Saining, and Zhuowen Tu. "Holistically-nested edge detection." Proceedings of the IEEE international conference on computer vision. 2015.
[24] He, Kaiming, et al.” Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[25] Abdul-Kareem Younis, H., & Ahmed Uraibi, Z. (2017). Design and Implementation of an Iris Recognition System. Journal of Al-Qadisiyah for Computer Science and Mathematics, 2(2), 89-107. Retrieved from
https://qu.edu.iq/journalcm/index.php/journalcm/article/view/226.
