Abstract
Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binarized Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary split is performed to divide the eight-bit binary of every pixel into four bit Least Significant Bits and four bit Most Significant Bits. The DWT is applied on four bit MSB to extract the iris features. Then ICA is applied on approximate sub band to extract the significant details of iris. The obtained features are then applied on BSIF to obtain the enhanced response with final features. Finally, generated features are then matched with test features using Euclidean distance classifier on CASIA v1.0 database to analyse the proposed iris model.
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References
Afifa Afreen, M., Diana Judith, I.: Iris recognition using hybrid technique, methods of moment and K means algorithm. In: International Conference on Advancements in Computing Technologies, vol. 4, no. 2, pp. 1–4 (2018)
Sali, J., Kadu, C.B.: Reliable human identification using iris as a biometric. Int. J. Sci. Eng. Manag. 1(8), 30–33 (2016)
Nagireddy, B.: Iris recognition using graphical user interface. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 7(1), 66–72 (2018)
Bansal, A., Agarwal, R., Sharma, R.K.: Iris images based pre-diagnostic tool to predict obstructive lung diseases. In: International Conference on Biomedical Research, pp. 517–522 (2018)
Singh, M., Sharma, S.: Iris recognition using Savitzky-Golay filter for better security outcomes. Int. J. Innovative Res. Comput. Commun. Eng. 6(6), 6329–6336 (2018)
Thirumurugan, P., Mohanbabu, G.: Iris recognition using wavelet transformation techniques. Int. J. Comput. Sci. Mob. Comput. 3(1), 75–83 (2014)
Kumar, A., Potnis, A., Pratap Singh, A.: Iris recognition and feature extraction in iris recognition system by employing 2D DCT. Int. Res. J. Eng. Technol. 3(12), 503–510 (2016)
Patil, R.B., Patodkar, N., Deshmukh, P.D.: Comparative performance analysis of feature extraction techniques of ıris recognition. In: International Conference on Recent Advances in Computer Science, Engineering and Technology, pp. 22–26 (2017)
Kovoor, B.C., Supriya, M.H., Poulose Jacob, K.: Iris biometric recognition system employing canny operator. In: International Conference of Computer Science & Information Technology, pp. 65–74 (2013)
Bansal, A., Agarwal, R., Sharma, R.K.: Iris biometric recognition system employing canny operator. Int. J. Indian Acad. Sci. 41(5), 507–518 (2016)
Raja, K.B., Ragahavendra, R., Busch, C.: Scale-level score fusion of steered pyramid features for cross-spectral periocular verification. In: IEEE International Conference on Information Fusion, pp. 1–5 (2017)
Joshi, K., Agrawal, S.: An iris recognition based on robust intrusion detection. In: Annual IEEE India Conference, pp. 1–6 (2016)
Arunalatha, J.S., Rangaswamy, Y., Shaila, K., Raja, K.B., Anvekar, D., Venugopal, K.R., Iyengar, S.S., Patnaik, L.M.: Iris recognition using hybrid domain features. In: Annual IEEE India Conference, pp. 1–5 (2015)
Gale, A.G., Salankar, S.S.: Evolution of performance analysis of ıris recognition system by using hybrid method of feature extraction and matching by hybrid classifier for ıris recognition system. In: IEEE International Conference on Electrical, Electronics and Optimization Techniques, pp. 3259–3263 (2016)
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Harakannanavar, S.S., Prashanth, C.R., Raja, K.B. (2020). MSB Based Iris Recognition Using Multiple Feature Descriptors. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_68
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DOI: https://doi.org/10.1007/978-3-030-30465-2_68
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