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Cybernetics and Systems
An International Journal
Volume 55, 2024 - Issue 2
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Research Articles

A New Design of Iris Recognition Using Hough Transform with K-Means Clustering and Enhanced Faster R-CNN

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References

  • Abate, A. F., S. Barra, L. Gallo, and F. Narducci. 2017. Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices. Pattern Recognition Letters 91:37–43. doi:10.1016/j.patrec.2017.02.002.
  • Abiyev, R. H., and K. Altunkaya. 2008. Personal iris recognition using neural network. International Journal of Security and Its Applications 2 (2):41–50.
  • Adamović, S., V. Miškovic, N. Maček, M. Milosavljević, M. Šarac, M. Saračević, and M. Gnjatović. 2020. An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Future Generation Computer Systems 107:144–57. doi:10.1016/j.future.2020.01.056.
  • Ahmadi, N., M. Nilashi, S. Samad, T. A. Rashid, and H. Ahmadi. 2019. An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology 120:105701. doi:10.1016/j.optlastec.2019.105701.
  • Alaslani, M., and L. Elrefaei. 2018. Convolutional neural network based feature extraction for IRIS recognition. International Journal of Computer Science and Information Technology 10 (2):65–78.
  • Al-Waisy, A. S., R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. M. Nagem. 2018. A multi-biometric iris recognition system based on a deep learning approach. Pattern Analysis and Applications 21 (3):783–802. doi:10.1007/s10044-017-0656-1.
  • Babu, G., and P. A. Khayum. 2022. Elephant herding with whale optimization enabled ORB features and CNN for iris recognition. Multimedia Tools and Applications 81 (4):5761–94. doi:10.1007/s11042-021-11746-7.
  • Bojja, G. R., M. Ofori, J. Liu, and L. S. Ambati. 2020. Early public outlook on the coronavirus disease (COVID-19): A social media study. In Social Media Analysis on Coronavirus (COVID-19).
  • Boutros, F., N. Damer, K. Raja, R. Ramachandra, F. Kirchbuchner, and A. Kuijper. 2020. Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation. Image and Vision Computing 104:104007. doi:10.1016/j.imavis.2020.104007.
  • Brown, D. 2022. Deep face-iris recognition using robust image segmentation and hyperparameter tuning. Computer Networks and Inventive Communication Technologies 75:259–75.
  • Chen, Y., H. Gan, Z. Zeng, and H. Chen. 2022. DADCNet: Dual attention densely connected network for more accurate real iris region segmentation. International Journal of Intelligent Systems 37 (1):829–58. doi:10.1002/int.22649.
  • Chen, Y., C. Wu, and Y. Wang. 2020. T-Center: A novel feature extraction approach towards large-scale iris recognition. IEEE Access 8:32365–75. doi:10.1109/ACCESS.2020.2973433.
  • Choudhary, M., V. Tiwari, and U. Venkanna. 2022. Unraveling deep learning performance in cross-sensor iris recognition. In ICDSMLA, Vol. 783, 93–104.
  • Dhanachandra, N., K. Manglem, and Y. J. Chanu. 2015. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. In IMCIP.
  • Dias, L. O., C. R. Bom, E. L. Faria, M. B. Valentín, M. D. Correia, M. P. de Albuquerque, M. P. de Albuquerque, and J. M. Coelho. 2020. Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks. Journal of Petroleum Science and Engineering 191:107099. doi:10.1016/j.petrol.2020.107099.
  • Ghosh, R. 2020. A Recurrent Neural Network based deep learning model for offline signature verification and recognition system. Expert Systems with Applications 168:114249. doi:10.1016/j.eswa.2020.114249.
  • Gupta, G. 2011. Algorithm for image processing using improved median filter and comparison of mean, median and improved median filter. International Journal of Soft Computing and Engineering (IJSCE) 1 (5):304–11.
  • Gupta, R., and K. Gupta. 2016. Iris recognition using templates fusion with weighted majority voting. International Journal of Image and Data Fusion 7 (4):325–38. doi:10.1080/19479832.2014.961973.
  • Illuri, B., and D. Jose. 2021. Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks. Journal of Ambient Intelligence and Humanized Computing 12 (5):5427–41. doi:10.1007/s12652-020-02030-x.
  • Jayanthi, J., E. L. Lydia, N. Krishnaraj, T. Jayasankar, R. L. Babu, and R. A. Suji. 2021. An effective deep learning features based integrated framework for iris detection and recognition. Journal of Ambient Intelligence and Humanized Computing 12 (3):3271–81. doi:10.1007/s12652-020-02172-y.
  • Kagawade, V. C, and S. A. Angadi. 2022. A new scheme of polar Fast Fourier Transform Code for iris recognition through symbolic modelling approach. Expert Systems with Applications 197 (116745):116745. doi:10.1016/j.eswa.2022.116745.
  • Kumar, A., and A. Passi. 2010. Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognition 43 (3):1016–26. doi:10.1016/j.patcog.2009.08.016.
  • Labati, R. D., A. Genovese, V. Piuri, F. Scotti, and S. Vishwakarma. 2020. I-SOCIAL-DB: A labeled database of images collected from websites and social media for Iris recognition. Image and Vision Computing 105:104058. doi:10.1016/j.imavis.2020.104058.
  • Lee, J.-C., Y. Su, T.-M. Tu, and C.-P. Chang. 2010. A novel approach to image quality assessment in iris recognition systems. The Imaging Science Journal 58 (3):136–45. doi:10.1179/136821909X12581187860059.
  • Liang, H., Z. Chen, H. Zhang, J. Liu, X. Li, L. Xiao, and Z. He. 2019. Multi-pyramid optimized mask R-CNN for iris detection and segmentation. Biometric Recognition 11818:329–36. doi:10.1007/978-3-030-31456-9_37.
  • Lin, W.-T., C.-C. Liu, and S.-Y. Chen. 2009. Fast and robust iris recognition. The Imaging Science Journal 57 (3):128–39. doi:10.1179/174313109X459878.
  • Liu, M., Z. Zhou, P. Shang, and D. Xu. 2020. Fuzzified image enhancement for deep learning in iris recognition. IEEE Transactions on Fuzzy Systems 28 (1):92–9. doi:10.1109/TFUZZ.2019.2912576.
  • Madhusmita, R., and S. S. K. Padhy. 2020. Elephant herding optimization for multiprocessor task scheduling in heterogeneous environment. Computational Intelligence in Pattern Recognition 217–29.
  • Minaee, S., and A. Abdolrashidi. 2019. DeepIris: Iris recognition using a deep learning approach. Computer Vision and Pattern Recognition. doi:10.48550/arXiv.1907.09380.
  • Minaee, S., A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang. 2019. Biometrics recognition using deep learning: A survey. Computer Vision and Pattern Recognition. doi:10.48550/arXiv.1912.00271.
  • Minaee, S., A. Abdolrashidiy, and Y. Wang. 2016. An experimental study of deep convolutional features for iris recognition. In IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 1–6.
  • Mirjalili, S., and A. Lewis. 2016. The Whale Optimization Algorithm. Advances in Engineering Software 95:51–67. doi:10.1016/j.advengsoft.2016.01.008.
  • Nguyen, K., C. Fookes, A. Ross, and S. Sridharan. 2018. Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6:18848–55. doi:10.1109/ACCESS.2017.2784352.
  • Nguyen, K., C. Fookes, and S. Sridharan. 2020. Constrained design of deep iris networks. IEEE Transactions on Image Processing 29:7166–75. doi:10.1109/TIP.2020.2999211.
  • Nguyen, K., C. Fookes, S. Sridharan, and A. Ross. 2022. Complex-valued Iris Recognition Network. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2022.3152857.
  • Oktiana, M., T. Horiuchi, K. Hirai, K. Saddami, F. Arnia, Y. Away, and K. Munadi. 2020. Cross-spectral iris recognition using phase-based matching and homomorphic filtering. Heliyon 6 (2):e03407. doi:10.1016/j.heliyon.2020.e03407.
  • Pizer, S. M., E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld. 1987. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39 (3):355–68. doi:10.1016/S0734-189X(87)80186-X.
  • Proença, H., and J. C. Neves. 2018. Deep-PRWIS: Periocular recognition without the iris and sclera using deep learning frameworks. IEEE Transactions on Information Forensics and Security 13 (4):888–96. doi:10.1109/TIFS.2017.2771230.
  • Raja, K. B., R. Raghavendra, S. Venkatesh, and C. Busch. 2017. Multi-patch deep sparse histograms for iris recognition in visible spectrum using collaborative subspace for robust verification. Pattern Recognition Letters 91:27–36. doi:10.1016/j.patrec.2016.12.025.
  • Rajakumar, B. R. 2013a. Impact of static and adaptive mutation techniques on genetic algorithm. International Journal of Hybrid Intelligent Systems 10 (1):11–22. doi:10.3233/HIS-120161.
  • Rajakumar, B. R. 2013b. Static and adaptive mutation techniques for genetic algorithm: A systematic comparative analysis. International Journal of Computational Science and Engineering 8 (2):180–93. doi:10.1504/IJCSE.2013.053087.
  • Ramesh, D., D. Jose, R. Keerthana, and V. Krishnaveni. 2018. Detection of pulmonary nodules using thresholding and fractal analysis. Computational Vision and Bio Inspired Computing 28:937–46. doi:10.1007/978-3-319-71767-8_80.
  • Ren, X., Q. Tian, J. Zhang, S. Wu, and Y. Zeng. 2008. Iris recognition based on key image feature extraction. Journal of Medical Engineering & Technology 32 (3):228–34. doi:10.1080/03091900701605425.
  • Sardar, M., S. Banerjee, and S. Mitra. 2020. Iris segmentation using interactive deep learning. IEEE Access 8:219322–30. doi:10.1109/ACCESS.2020.3041519.
  • Shadravan, S., H. R. Naji, and V. K. Bardsiri. 2019. The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence 80:20–34. doi:10.1016/j.engappai.2019.01.001.
  • Shuai, L., L. Yuanning, Z. Xiaodong, H. Guang, C. Jingwei, Z. Qixian, W. Zukang, L. Xinlong, and W. Chaoqun. 2020. Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous iris recognition. IEEE Access 8:53321–45. doi:10.1109/ACCESS.2020.2981555.
  • Sulochana, C. H., and S. Selvan. 2010. Rotation compensation iris recognition system using multi-scale directional filter bank and 2DPCA. International Journal of Modelling and Simulation 30 (4):499–505. doi:10.1080/02286203.2010.11442600.
  • Tabjula, J. L., S. Kanakambaran, S. Kalyani, P. Rajagopal, and B. Srinivasan. 2021a. Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Structural Control and Health Monitering 28 (3). doi:10.1002/stc.2690.
  • Tabjula, J., S. Kalyani, P. Rajagopal, and B. Srinivasan. 2021b. Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves. Structural Health Monitoring. doi:10.1177/14759217211073335.
  • Trokielewicz, M., A. Czajka, and P. Maciejewicz. 2020. Post-mortem iris recognition with deep-learning-based image segmentation. Image and Vision Computing 94:103866. doi:10.1016/j.imavis.2019.103866.
  • Umer, S., B. C. Dhara, and B. Chanda. 2018. An iris recognition system based on analysis of textural edgeness descriptors. IETE Technical Review 35 (2):145–56. doi:10.1080/02564602.2016.1265904.
  • Wang, K., and A. Kumar. 2019. Toward more accurate iris recognition using dilated residual features. IEEE Transactions on Information Forensics and Security 14 (12):3233–45. doi:10.1109/TIFS.2019.2913234.
  • Wang, K., and A. Kumar. 2019. Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recognition 86:85–98. doi:10.1016/j.patcog.2018.08.010.
  • Winston, J. J., D. J. Hemanth, A. Angelopoulou, and E. Kapetanios. 2022. Anastassia Angelopoulou & Epaminondas Kapetanios. Hybrid deep convolutional neural models for iris image recognition. Multimedia Tools and Applications 81 (7):9481–503. doi:10.1007/s11042-021-11482-y.
  • Zarie, M., A. Jahedsaravani, and M. Massinaei. 2020. Flotation froth image classification using convolutional neural networks. Minerals Engineering 155:106443. doi:10.1016/j.mineng.2020.106443.
  • Zhao, T., Y. Liu, G. Huo, and X. Zhu. 2019. A deep learning iris recognition method based on capsule network architecture. IEEE Access 7:49691–701. doi:10.1109/ACCESS.2019.2911056.
  • Zhao, Z., and K. Ajay. 2015. An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In Proceedings of the IEEE International Conference on Computer Vision, 3828–36.

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