Abstract
Optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed Teaching Learning Based Optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. Teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods Standard Iris Recognition System and Genetic Algorithm optimized Iris Recognition System. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hollingsworth K.P., Bowyer K.W., and Flynn P.J., “Using Fragile Bit Coincidence to Improve Iris Recognition,” Proc. IEEE Third Int’l Conf. Biometrics: Theory, Applications, and Systems, pp. 1-6 (2009).
Daugman J., “How Iris Recognition Works,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30(2004).
Rao R.V. and Kalyankar V.D., “Multi-objective multi-parameteroptimization of the industrial LBW process using a new optimization algorithm”, Journal of Engineering Manufacture, 2012b, DOI: 10.1177/ 0954405411435865
Bowyer K.W., Hollingsworth K.P., and Flynn P.J., “Image Understanding for Iris Biometrics: A Survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307(2008)
Bolle R.M., Pankanti S., Connell J.H., and Ratha N., “Iris Individuality: A Partial Iris Model,” Proc. Int’l Conf., Pattern Recognition, pp. 927-930 (2004)
Hollingsworth K.P., Bowyer K.W., and Flynn P.J., “All Iris Code Bits Are Not Created Equal,” Proc. IEEE Int’l Conf. Biometrics:Theory, Applications and Systems, pp. 1-6 (2007).
Hollingsworth K.P., Bowyer K.W., and Flynn P.J., “The Best Bits in an Iris Code,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol 31, no. 6, pp. 964-973(2009)
Barzegar N. and Moin M.S., “A New User Dependent Iris Recognition System Based on an Area Preserving Pointwise Level Set Segmentation Approach,” EURASIP J. Advances in Signal Processing, pp. 1-13 (2009)
Dozier G., Bell D., Barnes L., and Bryant K., “Refining Iris Templates via Weighted Bit Consistency,” Proc. Midwest Artificial Intelligence and Cognitive Science Conference, pp. 1-5 (2009).
Dozier G., Frederiksen K., Meeks R., Savvides M., Bryant K., Hopes D., and Munemoto T., “Minimizing the Number of Bits Needed for Iris Recognition via Bit Inconsistency and Grit,” Proc. IEEE Workshop Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, pp. 30-37(2009).
Daugman J., “Demodulation by complex-valued wavelets for stochastic pattern recognition,” Internat. J. Wavelets, Multi-Res. And Info. Processing, vol. 1, pp.1–17(2003).
Adam M., Rossant F., Mikovicova B., and Amiel F., “Iris identification based on a local analysis of the iris texture”. Proceedings of 6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009), pp523-528 (2009).
Adjedj M., Bringer J., Chabanne H., and Kindarji B., “Biometric Identification over Encrypted Data Made Feasible,” Information Systems Security: Lecture Notes in Computer Science, pp86-100 (2009).
Agrawal N.and Savvides M., “Biometric data hiding: A 3 factor authentication approach to verify identity with a single image using steganography, encryption and matching”.IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops 2009), pp85-92 (2009).
Alghamdi A.S., Ullah H., Mahmud M., and Khan M.K., “Bio-chaotic Stream Cipher-Based Iris Image Encryption”. International Conference on Computational Science and Engineering(CSE ’09), pp739-744 (2009)
Al-Qunaieer F.S. and Ghouti L., “Color Iris Recognition Using Hypercomplex Gabor Wavelets”. Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS ’09), pp18-19 (2009).
Baig A., Bouridane A., Kurugollu F., and Qu G., “Fingerprint-iris fusion based identification system using a single Hamming distance”. Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS ’09), pp 9-12 (2009).
Baker S., Bowyer K., and Flynn P., “Empirical Evidence for Correct Iris Match Score Degradation with Increased Time-Lapse between Gallery and Probe Matches”. Advances in Biometrics: Lecture Notes in Computer Science, pp 1170-1179 (2009).
Baker S. E., Hentz A., Bowyer K. W., and Flynn P. J., “Degradation of Iris Recognition Performance Due to Non-Cosmetic Prescription Contact Lenses”. Computer Vision and Image Understanding, pp 1030-1044 (2010).
Bastys A., Kranauskas J., and Masiulis R., “Iris Matching by Local Extremum Points of Multiscale Taylor Expansion,” Advances in Biometrics: Lecture Notes in Computer Science pp 1070-1079 (2009).
Suresh Chandra Satapathy, Anima Naik and K Parvathi” A teaching learning based optimization based on Orthogonal design for solving global optimization problems” in Springer Open Journal (2013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Agrawal, S., Sharma, S., Silakari, S. (2015). Teaching Learning Based Optimization (TLBO) Based Improved Iris Recognition System. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_105
Download citation
DOI: https://doi.org/10.1007/978-3-319-08422-0_105
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
eBook Packages: EngineeringEngineering (R0)