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Iris Recognition using Convolutional Neural Network

by Md. Shafiul Azam, Humayan Kabir Rana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 12
Year of Publication: 2020
Authors: Md. Shafiul Azam, Humayan Kabir Rana
10.5120/ijca2020920602

Md. Shafiul Azam, Humayan Kabir Rana . Iris Recognition using Convolutional Neural Network. International Journal of Computer Applications. 175, 12 ( Aug 2020), 24-28. DOI=10.5120/ijca2020920602

@article{ 10.5120/ijca2020920602,
author = { Md. Shafiul Azam, Humayan Kabir Rana },
title = { Iris Recognition using Convolutional Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 12 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number12/31505-2020920602/ },
doi = { 10.5120/ijca2020920602 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:51.167877+05:30
%A Md. Shafiul Azam
%A Humayan Kabir Rana
%T Iris Recognition using Convolutional Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 12
%P 24-28
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is indispensable to ensure security biometrically in the most authentication and identification scenario. Iris recognition regarded as the most reliable biometric recognition due to its stable and extraordinary variation in texture. The unique patterns are used in iris recognition to identify individuals in requiring a high level of security. This paper explores an efficient technique that uses convolutional neural network (CNN) and support vector machine (SVM) for feature extraction and classification respectively to increase the efficiency of recognition. The proposed technique has been successfully applied and also clearly demonstrates the performance of the experimental evaluation on iris images from the CASIA database.

References
  1. SaiyedUmer, Bibhas Chandra Dhara, BhabatoshChanda, “A Novel Cance-lable Iris Recognition System Based on Feature Learning Techniques”, Elsevier Information Sciences, vol. 406-407, pp. 102-118, 2017.
  2. Imran Naseema, AffanAleemb, RobertoTogneric and Mohammed Bennamoun, “Iris recognition using class-specific dictionaries”, Elsevier Computers and Electrical Engineering, vol. 62, pp. 178-193, 2016.
  3. Chiara Galdia, Michele Nappib and Jean-Luc Dugelaya, “Multimodal authentication on smartphones: Combining iris and sensor recognition for a double check of user identity”, Elsevier Pattern Recognition Letters, vol. 82, pp. 144-153, 2016.
  4. HimanshuSrivastava, “A Comparison Based Study on Biometrics for Human Recognition”, IOSR Journal of Computer Engineering, vol. 15, no. 1, 2013.
  5. Rana HK, Azam MS, Akhtar MR, “Iris recognition system using PCA based on DWT”, SM Journal of Biometrics & Biostatistics vol. 2, no. 3, pp.: 1015, DOI 10.5281/zenodo.2580202, 2017.
  6. Tieniu Tan ZS, Center for biometrics and security research. Available at http:// www.cbsr.ia.ac.cn/ china/ Iris%20Databases%20CH.asp, 2015 (accessed on 6 May 2020).
  7. PrateekVerma, MaheedharDubey, SomakBasu, Praveen Verma, “Hough Transform Method for Iris Recognition-A Biometric Approach”, International Journal of Engineering and Innovative Technology (IJEIT), Volume 1, Issue 6, June 2012.
  8. PrateekVerma, MaheedharDubey, Praveen Verma, SomakBasu, “daughman‟s algorithm method for iris Recognition-a biometric approach”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 6, June 2012.
  9. John G. Daugman. “High condence visual recognition of persons by a test of statistical independence”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 15(11):1148-1161, Nov. 1993.
  10. Hamd, M. H. and Ahmed, S. K., “Biometric system design for iris recognition using intelligent algorithms”, International Journal of Modern Education and Computer Science, vol. 10, no. 3, pp. 9–16,2018.
  11. O. Oyedotun and A. Khashman, "Iris nevus diagnosis: convolutional neural network and deep belief network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, pp. 1106-1115, 2017.
  12. D. Scherer, A. Müller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," Artificial Neural Networks–ICANN 2010, pp. 92-101, 2010.
  13. A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. Nagem, "A multi-biometric irisrecognition system based on a deep learning approach," Pattern Analysis and Applications, pp. 1-20, 2017.
  14. C. L. Lam and M. Eizenman, "Convolutional neural networks for eye detection in remote gaze estimation systems," Proceedings of the International Multi Conference of Engineers and Computer Scientists. Vol. 1. Citeseer, 2008.
  15. L. Ma, T. Tan, Y. Wang and D. Zhang, “Efficient iris recognition by characterizing key local variation”, IEEE Trans on Image Process, vol. 13, no. 6, pp. 739-750, IEEE, 2004.
  16. DeepthiRampally, “ris Recognition Based on Feature Extraction”, Department of Electrical and Computer Engineering, Kansas State University, 2010.
  17. K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, "Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective," IEEE Access, 2017.
  18. A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A.Nagem, "A multi-biometric iris recognition system based on a deep learning approach," Pattern Analysis and Applications, pp. 1-20, 2017.
  19. J. Nagi, F. Ducatelle, G. A. Di Caro, D. Cireşan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, and L.M. Gambardella, "Max-pooling convolutional neural networks for vision-based hand gesture recognition," in Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on, 2011, pp. 342-347.
  20. Rana HK, Azam MS, Akhtar MR, Quinn JMW, Moni MA, “A fast iris recognition system through optimumfeature extraction”, PeerJ Computer Science, 5:e184 https://doi.org/10.7717/peerj-cs.184, 2019.
  21. Johora, FatemaTuj, Mehdi Hassan Jony, MdShakhawatHossain, and HumayanKabir. "lung cancer detection using marker-controlled watershed with svm", GUB Journal of Science and Engineering, vol. 5, no. 1, pp. 24-30, 2018.
  22. Jony, Mehdi Hassan, et al. "Detection of Lung Cancer from CT Scan Images using GLCM and SVM." 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019.
  23. Firake SG, Mahajan PM, “Comparison of iris recognition using gabor wavelet,principal component analysis and independent component analysis”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 4, no. 6, pp. 12334-12342, DOI 10.15680/IJIRCCE.2016.0406293, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition Hough Transformation Daugman’s Rubber Sheet Model CNN and SVM

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