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Elephant herding with whale optimization enabled ORB features and CNN for Iris recognition

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Abstract

Iris biometrics is one of the frequently used biometrics for security purposes, and it provides the appropriate tools for identifying humans. Deep learners when used along with iris recognition can improve accuracy, automatic learning, and generalization ability. Despite the advantages of deep learners, some problems like time complexity and computational efforts exist in deep learners. In order to solve these obstacles, this paper intends to develop the intelligent iris recognition model based on the concepts merged with multi-objective feature selection and deep learning. The main phases of the proposed model involve (a) Pre-processing, (b) Iris Segmentation, (c) optimal ORB point selection and feature vector generation, and (d) optimal recognition. Here, the pre-processing of the image is performed by filtering and contrast enhancement techniques. Further, the iris segmentation is done by few approaches such as reflection moving, iris localization, and Hough-transform-based segmentation. Once the segmentation of the iris is finished, feature extraction is carried out by optimized Oriented FAST and rotated BRIEF (ORB) features concerning on multi-objective function. Finally, the optimized Convolutional Neural Network (CNN) is used for iris recognition. Here, the hybrid algorithm with the integration of two meta-heuristic algorithms like Elephant Herding Optimization (EHO), and Whale Optimization Algorithm (WOA) called Elephant Herding with Whale Optimization Algorithm (EH-WOA) is utilized for performing the optimized ORB feature selection and optimized CNN. The experiments are conducted on a benchmark datasets like IIT Delhi (IITD) Iris database, and MMU iris dataset to analyze the performance of the proposed model over the different network structures for accurate iris recognition. Through the experimental analysis, the proposed optimized CNN + EH-WOA had better values in terms of accuracy, which is 0.98% higher than SVM, 0.92% higher than KNN, 0.88% higher NN, and 0.85% higher than CNN respectively for the learning % as 75 for MMU iris dataset. Similarly, several performance measures have considered for showing the efficiency of the designed model.

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References

  1. Abiyev RH, Altunkaya K (2008) Personal iris recognition using neural network. Int J Secur Appl 2(2):41–50

    Google Scholar 

  2. Azam MS, Rana HK (2020) Iris recognition using convolutional neural network. Int J Comput Appl 175(12):24–28

    Google Scholar 

  3. Chen C-H, Chu C-T (2009) High performance iris recognition based on 1-D circular feature extraction and CNN+PSOPNN classier. Expert Syst Appl 36(7):10351–10356

    Article  Google Scholar 

  4. Chen Y, Liu Y, Zhu X, Chen H, He F, Pang Y (2014) Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion”, Sci World J, 2014

  5. Chen Y, Liu Y, Zhu X, Chen H, He F, Pang Y (2014) Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion. Scientific World J

  6. Chen Y, Cheng W, Wang Y (2020) T-center: a novel feature extraction approach towards large-scale Iris recognition. IEEE Access 8:32365–32375

    Article  Google Scholar 

  7. Daugman J (2004) How iris recognition works? IEEE Trans Circuits Syst Video Technol 14:21–30

    Article  Google Scholar 

  8. Daugman J (2007) New methods in iris recognition. IEEE Trans Syst Man and Cybernetics 37:1167–1175

    Article  Google Scholar 

  9. De Marsico M, Petrosino A, Ricciardi S (2016) Iris recognition through machine learning techniques: a survey. Pattern Recogn Lett 82:106–115

    Article  Google Scholar 

  10. Gupta G (2011) Algorithm for image processing using improved median filter and comparison of mean, median and improved median filter. Int J Soft Comput Eng (IJSCE) 1(5):304–311

    Google Scholar 

  11. Kaur B, Singh S, Kumar J (2018) Iris recognition using Zernike moments and polar harmonic transforms. Arabian J Sci Eng 43:7209–7218

    Article  Google Scholar 

  12. Kulkarni SB, Kulkarni RB, Kulkarni UP, Hegadi RS (2014) GLCM-based multiclass iris recognition using FKNN and KNN. Int J Image Graphics 14(03):1450010

    Article  Google Scholar 

  13. Kumar, A. And Passi, A., “Comparison and combination of iris matchers for reliable personal authentication,” Pattern Recogn, vol. 43, no. 3, pp.1016–1026, 2010.

    Article  Google Scholar 

  14. Li Y-H, Huang P-J (2017) “An accurate and efcient user authentication mechanism on smart glasses based on Iris recognition”, Mobile. Inf Syst 2017

  15. Madhusmita R, Padhy SK (2020) “Elephant Herding Optimization for Multiprocessor Task Scheduling in Heterogeneous Environment”, Computational Intelligence in Pattern Recognition, pp 217–229

  16. Melin P, Sanchez D (2018) A Grey Wolf Optimization Algorithm for Modular Granular Neural Networks Applied to Iris Recognition. Hybrid Intelligent Systems, pp.282–293

  17. Min Beom Lee (2019) Yu Hwan Kim, and Kang Ryoung Park, “conditional generative adversarial network- based data augmentation for enhancement of Iris recognition accuracy”. IEEE Access 7:122134–122152

    Article  Google Scholar 

  18. Nalla PR, Kumar A (2017) Toward more accurate Iris recognition using cross-spectral matching. IEEE Trans Image Process 26(1):208–221

    Article  MathSciNet  Google Scholar 

  19. Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6:18848–18855

    Article  Google Scholar 

  20. Nguyen DT, Pham TD, Lee YW, Park KR (2018) Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on NIR camera sensor. Sensors 18(8):2601

    Article  Google Scholar 

  21. Oktiana M, Saddami K, Arnia F, Away Y, Hirai K, Horiuchi T, Munadi K (2019) Advances in cross-spectral Iris recognition using integrated Gradientface-based normalization. IEEE Access 7:130484–130494

    Article  Google Scholar 

  22. Pillai JK, Puertas M, Chellappa R (2014) Cross-sensor iris recognition through kernel learning. IEEE Trans Pattern Analysis Mach Intell 36(1):73–85

    Article  Google Scholar 

  23. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vision Graphics Image Process 39(3):355–368

    Article  Google Scholar 

  24. Proenca H, Alexandre LA (2007) Toward noncooperative iris recognition: a classication approach using multiple signatures. IEEE Trans Pattern Anal Mach Intell 29(4):607–612

    Article  Google Scholar 

  25. Roy, K. And Bhattacharya, P., “Iris recognition with support vector machines,” In International conference on biometrics, Springer, Berlin, pp. 486–492, 2006.

    Google Scholar 

  26. Saini TK, Bhushan S, Jangra S (2021) An Improved Biometric Fusion System of Fingerprint and Face using Whale Optimization. Int J Advanced Comput Sci Appl 12(1)

  27. Shende K, Sonavane SS "CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications", Wireless Networks, pp. 1–19.

  28. Shuai L, Yuanning L, Xiaodong Z, Guang H, Jingwei C, Qixian Z, Zukang W, Zhiyi D (2019) Statistical cognitive learning and security output protocol for multi-state iris recognition. IEEE Access 7:132871–132893

    Article  Google Scholar 

  29. Shuai L, Yuanning L, Xiaodong Z, Guang H, Jingwei C, Zhang Qixian W, Zukang LX, Chaoqun W (2020) Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous Iris recognition. IEEE Access 8:53321–53345

    Article  Google Scholar 

  30. Sun Z, Tan T (2009) Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell 31(12):2211–2226

    Article  Google Scholar 

  31. Tan T, He Z, Sun Z (2010) Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis Comput 28(2):223–230

    Article  Google Scholar 

  32. Wang K, Kumar A (2019) Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recogn 86:85–98

    Article  Google Scholar 

  33. Wang, R., Zhang, W., Shi, Y., Wang, X. And Cao, W., “GA-ORB: a new efficient feature extraction algorithm for multispectral images based on geometric algebra,” IEEE Access, vol. 7, pp.71235–71244, 2019.

    Article  Google Scholar 

  34. Wang C, Muhammad J, Wang Y, He Z, Sun Z (2020) Towards complete and accurate Iris segmentation using deep multi-task attention network for non-cooperative Iris recognition. IEEE Trans Inform Forensics Security 15:2944–2959

    Article  Google Scholar 

  35. Wildes RP et al (1996) A machine-vision system for iris recognition. Mach Vis Appl 9(1):1–8

    Article  Google Scholar 

  36. Yadav D, Kohli N, Doyle JS, Singh R, Vatsa M, Bowyer KW (2014) Unraveling the effect of textured contact lenses on iris recognition. IEEE Trans Inform Forensics Secur 9(5):851–862

    Article  Google Scholar 

  37. Yuanning L, Shuai L, Xiaodong Z (2019) Iris recognition algorithm based on feature weighted fusion. J Jilin Univ (Eng Technol Ed) 49(1):221–229

    Google Scholar 

  38. Zhao Z, Ajay K (2015) An accurate iris segmentation framework under relaxed imaging constraints using total variation model,” In Proceedings of the IEEE international conference on computer vision, pp. 3828–3836

  39. Zhao T, Liu Y, Huo G, Zhu X (2019) A deep learning Iris recognition method based on capsule network architecture. IEEE Biom Compend 7:49691–49701

    Google Scholar 

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Electronics & Communication Engineering, JNTU, Anantapur, India

    Gorla Babu

  2. Electronics & Communication Engineering, G. Pullareddy Engineering College, Kurnool, India

    Pinjari Abdul Khayum

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  1. Gorla Babu
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  2. Pinjari Abdul Khayum
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Gorla Babu.

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Babu, G., Khayum, P.A. Elephant herding with whale optimization enabled ORB features and CNN for Iris recognition. Multimed Tools Appl 81, 5761–5794 (2022). https://doi.org/10.1007/s11042-021-11746-7

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  • DOI: https://doi.org/10.1007/s11042-021-11746-7

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