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An efficient CNN based encrypted Iris recognition approach in cognitive-IoT system

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Abstract

Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves a lot of security issues, especially in smart IoT-based applications. It increases the resistance of these systems against severe authentication attacks. In this paper, an efficient iris recognition model based on chaotic encryption and deep Convolutional Neural Networks (CNNs) is proposed for C-IoT applications. CNN is used to extract the deep iris features from the left and right eyes, which will be used as input features to a fully connected neural network with a Softmax classifier. CASIA V4 Interval dataset and Phoenix dataset are used to train the CNN model; to get the best tuning of network parameters. In this paper, the effect of adding different kinds of noise to iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by system users, or other system assaults, is discussed. This strategy of noisy encrypted iris images is evaluated over the internet environment. Chaotic encryption is utilized to secure the transmission of iris templates in the proposed model. The results showed that the proposed approach attains supreme accuracy compared to the existing approaches, it is obtained up to 99.24% and 100% with CASIA V4 and Phoenix datasets, respectively. The proposed model achieves satisfied and competitive results regard accuracy, and robustness among existing methods. Regards to recognition accuracy rate, this methodology shows low degradation of recognition accuracy rates in the case of using noised iris images. Likewise, the proposed method has a relatively low training time, which is a useful parameter in critical IoT based uses such as Tele-Medicine application.

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References

  1. Alaslani MG, Elrefaei LA (2018) Convolutional neural network based feature extraction for IRIS recognition. Int J Comput Sci Inf Technol 10(2):65–78

    Google Scholar 

  2. “AlexNet architecture.” (2020) https://neurohive.io/en/popular-networks/alexnet-imagenet-classification-with-deep-convolutional-neural-networks/ (accessed Oct. 17, 2020).

  3. Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TAM (2018) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Appl 21:783–802

    Article  MathSciNet  Google Scholar 

  4. Atlam HF, El-Din Hemdan E, Alenezi A, Alassafi MO, Wills GB (2020) “Internet of things forensics: a review,” Internet of Things

  5. Baqar M, Azhar S, Iqbal Z, Shakeel I, Ahmed L, Moinuddin M (2011) “Efficient iris recognition system based on dual boundary detection using robust variable learning rate Multilayer Feed Forward neural network,” in Proceedings of the 7th International Conference on Information Assurance and Security, IAS, pp. 326–330

  6. Bharath BV, Vilas AS, Manikantan K, Ramachandran S (2014) “Iris recognition using radon transform thresholding based feature extraction with Gradient-based Isolation as a pre-processing technique,” in 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–8

  7. Bishop CM (2006) Pattern Recognition and Machine Learning, 1st ed. Springer-Verlag New York

  8. Bolle RM, Connell JH, Pankanti S, Ratha NK, Senior AW (2004) Guide to Biometrics, 1st ed. Springer-Verlag New York

  9. Bowyer KW, Burge MJ, Eds. (2013) Handbook of Iris Recognition, 1st ed. Springer-Verlag London

  10. “CASIA iris dataset.” (2021) http://www.cbsr.ia.ac.cn/china/Iris Databases CH.asp (accessed Jan. 23, 2021)

  11. Courville A, Goodfellow I, Bengio Y (2016) Deep Learning, 1st ed. The MIT Press

  12. Dhage SS, Hegde SS, Manikantan K, Ramachandran S (2015) Dwt-based feature extraction and radon transform based contrast enhancement for improved iris recognition. Procedia Comput Sci 45:256–265

    Article  Google Scholar 

  13. Dobeš M, Machala L, Tichavský P, Pospíšil J (2004) Human eye iris recognition using the mutual information. Optik (Stuttg) 115(9):399–404

    Article  Google Scholar 

  14. Dobeš M, Martinek J, Skoupil D, Dobešová Z, Pospíšil J (2006) Human eye localization using the modified Hough transform. Optik (Stuttg). 117(10):468–473

    Article  Google Scholar 

  15. Ezz El-Din H, Manjaiah DH (2017) Internet of things in cloud computing. Internet of things: novel advances and envisioned applications. Springer, Cham:299–311

  16. Ezz El-Din H, Manjaiah DH (2017) Internet of nano things and industrial internet of things. Internet of things: novel advances and envisioned applications. Springer, Cham:109–123

  17. Gad R, Zorkany M, EL-Sayed A, EL-Fishawy N (2016) An efficient approach for simple Iris localization and normalization technique. Menoufia J Electron Eng Res 25(2):213–224

    Article  Google Scholar 

  18. Gad R, Talha M, El-sayed A, EL-Fishawy N, Muhammad G, Zorkany M (2018) Iris recognition using multi-algorithmic approaches for cognitive internet of things (CIoT) framework. Futur Gener Comput Syst 89:178–191

    Article  Google Scholar 

  19. Gad R, Abd El-Latif AA, Elseuofi S, Ibrahim HM, Elmezain M, Said W (2019) “IoT Security Based on Iris Verification Using Multi-Algorithm Feature Level Fusion Scheme,” in 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019

  20. Haykin S (2008) Neural Networks and Learning Machines 3rd ed. Pearson

  21. “Inception V3 architecture.” (2020) https://keras.io/api/applications/inceptionv3/ (accessed Oct. 17, 2020).

  22. Jain AK, Ross AA, and Nandakumar K (2011) Introduction to Biometrics, 1st ed. Springer US

  23. Khanam R, Haseen Z, Rahman N, Singh J (2019) Performance analysis of iris recognition system. Adv Intell Syst Comput 847:159–171

    Google Scholar 

  24. Lozej J, Stepec D, Struc V, Peer P (2019) “Influence of segmentation on deep iris recognition performance,” in 7th International Workshop on Biometrics and Forensics, IWBF

  25. Minaee S, Abdolrashidiy A, Wang Y (2016) “An experimental study of deep convolutional features for iris recognition,” in IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 37–42

  26. Mitchell TM (1997) Machine Learning, 1st ed. McGraw-Hill Education

  27. Nalla PR, Chalavadi KM (2015) Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications. Springerplus 4(1):1–10

    Article  Google Scholar 

  28. Parker TS, Chua L (1989) Practical Numerical Algorithms for Chaotic Systems, 1st ed. Springer-Verlag New York

  29. “Phoenix iris Dataset.” (2019) http://phoenix.inf.upol.cz/iris/ (accessed Nov. 06, 2019).

  30. Proença H, Alexandre LA (2005) “UBIRIS: A noisy iris image database,” in International Conference on Image Analysis and Processing, pp. 970–977

  31. Rana HK, Azam MS, Akhtar MR, Quinn JMW, and Moni MA (2019) “A fast iris recognition system through optimum feature extraction,” PeerJ Comput. Sci. 8 April

  32. “Raspberry Pi 2.” (2020) https://www.raspberrypi.org/products/raspberry-pi-2-model-b/ (accessed Sep. 14, 2020).

  33. “ResNet architecture.” (2020) https://neurohive.io/en/popular-networks/resnet/ (accessed Oct. 17, 2020).

  34. Rohith S, Bhat KNH, Sharma AN (2014) “Image encryption and decryption using chaotic key sequence generated by sequence of logistic map and sequence of states of Linear Feedback Shift Register,” in International Conference on Advances in Electronics, Computers and Communications (ICAECC ) 10–11.

  35. Saminathan K, Chakravarthy T, Chithra Devi M (2015) Iris recognition based on kernels of Suppoet vector machine. ICTACT J Soft Comput 5(2):889–895

    Article  Google Scholar 

  36. Singh G, Singh RK, Saha R, Agarwal N (2020) IWT based Iris recognition for image authentication. Procedia Comput. Sci. 171:1868–1876

    Article  Google Scholar 

  37. Stallings W (2016) Cryptography and network security: principles and practices, 7th ed. Pearson Education, Inc

  38. Stevens WR, Fenner B, Rudoff AM, Juszkiewicz K (2003) Unix Network Programming Volume 1: The Sockets Networking API, 3rd ed. Addison-Wesley Professional

  39. Umer S, Dhara BC, Chanda B (2016) Texture code matrix-based multi-instance iris recognition. Pattern Anal. Appl. 19(1):283–295

    Article  MathSciNet  Google Scholar 

  40. “VGG16 architecture.” (2020) https://neurohive.io/en/popular-networks/vgg16/ (accessed Oct. 17, 2020).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menof, Egypt

    Ahmed Sabry Shalaby, Ramadan Gad, Ezz El-Din Hemdan & Nawal El-Fishawy

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  1. Ahmed Sabry Shalaby
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  2. Ramadan Gad
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  3. Ezz El-Din Hemdan
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  4. Nawal El-Fishawy
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Correspondence to Ezz El-Din Hemdan.

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Shalaby, A.S., Gad, R., Hemdan, E.ED. et al. An efficient CNN based encrypted Iris recognition approach in cognitive-IoT system. Multimed Tools Appl 80, 26273–26296 (2021). https://doi.org/10.1007/s11042-021-10932-x

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