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Advances in Intelligent Systems and Computing

Volume 1089, 2015, Pages 735-739

23rd International Conference on Systems Engineering, ICSEng 2014; Las Vegas, NV; United States; 19

August 2014 through 21 August 2014; Code 107141

Teaching Learning Based Optimization (TLBO) Based Improved

Iris Recognition System (Conference Paper)

Agrawal, S. , Sharma, S. , Silakar i, S.

Department of Computer Science and Engineering, UIT, Rajiv Gandhi Proudyogiki Vishw avidyalaya, Bhopal, M.P, India

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. Springer

International Publishing Switzerland 2015.

Author keywords

Feature Extraction; Gabor Wavelet Transform; Genetic Algorithm; Hough Transform; Iris Recognition;

Teaching Learning Based Optimization

Indexed keywords

Engineering controlled terms:Algorithms; Biometrics; Genetic algorithms; Hough transforms; Image

matching; Systems engineering

Gabor wavelet transforms; Identification rates; Iris recognition; Iris recognition systems; Matching

process; Optimization techniques; Teaching-learning-based optimizations; Texture feature extraction

Engineering ma in hea ding:Feature extraction

ISSN: 21945357 ISBN: 978-331908421-3 Source Type: Book ser ies Original language: English

DOI: 10.1007/978-3-319-08422-0_105 Document Type: Conference Paper

Sponsors: ALDEC,NV Energy,Univers ity of Nevada Las Vegas ( UNLV) Publisher: Springer Verlag

References (21)

Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.

(2009) Proc. IEEE Third Int'l Conf. Biometrics: Theory, Applications, and Systems, pp. 1-6. Cited 6

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Daugman, J.

How Iris Recognition Works

(2004) IEEE Transactions on Circuits and Systems for Video Technology, 14 (1), pp. 21-30. Cited

1179 times.

doi: 10.1109/TCSVT.2003.818350

Rao, R.V., Kalyankar, V.D.

Multi-objective multi-parameter optimization of the industrial LBW process using

a new optimization algorithm

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Agrawal, N., Savvides, M.

Biometric data hiding: A 3 factor authentication approach to v erify identity with a

single image using ste ganography, encryption and matching

(2009) 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, art. no.

5204308, pp. 85-92. Cited 2 times.

ISBN: 978-142443991-1

doi: 10.1109/CVPR.2009.5204308

Alghamdi, A.S., Ullah, H., Mahmud, M., Khan, M.K.

Bio-chaotic stream cipher-based iris image encryption

(2009) Proceedings - 12th IEEE International Conference on Computational Science and

Engineering, CSE 2009, 2, art. no. 5282988, pp. 739-744. Cited 6 times.

ISBN: 978-076953823-5

doi: 10.1109/CSE.2009.317

Al-Qunaieer, F.S., Ghouti, L.

Color iris recognition using hypercomplex gabor wavelets

(2009) 2009 International Symposium on Bio-inspired, Learning, and Intelligent Systems for

Security, BLISS 2009, art. no. 5376870, pp. 18-19.

doi: 10.1109/BLISS.2009.19

Baig, A., Bouridane, A., Kurugollu, F., Qu, G.

Fingerprint - Iris fusion based ident ification system using a single hamming

distance matcher

(2009) 2009 International Symposium on Bio-inspired, Learning, and Intelligent Systems for

Security, BLISS 2009, art. no. 5376876, pp. 9-12. Cited 5 times.

doi: 10.1109/BLISS.2009.4

Baker, S.E., Bowyer, K.W., Flynn, P.J.

Empirical evidence for correct iris match score degradation with increased time-

lapse between gallery and probe matches(2009) Lecture Notes in Computer Science (including subseries Lecture Notes in A rtificial

Intelligence and Lecture Notes in Bioinformatics), 5558 LNCS, pp. 1170-1179. Cited 7 times.

ISBN: 3642017924; 978-364201792-6

doi: 10.1007/978-3-642-01793-3_118

Baker, S.E., Hentz, A., Bowyer, K.W., Flynn, P.J.

Degradation of iris recognition performance due to non-cosmetic prescription

contact lense s

(2010) Computer Vision and Image Understanding, 114 (9), pp. 1030-1044. Cited 13 times.

doi: 10.1016/j.cviu.2010.06.002

Bastys, A., Kranauskas, J., Masiulis, R.

Iris matching by local extremum points of multiscale taylor expansion

(2009) Lecture Notes in Computer Science (including subseries Lecture Notes in A rtificial

Intelligence and Lecture Notes in Bioinformatics), 5558 LNCS, pp. 1070-1079.

ISBN: 3642017924; 978-364201792-6

doi: 10.1007/978-3-642-01793-3_108

Satapathy, S.C., Naik, A., Parvathi, K.

(2013) Springer Open Journal

Agrawal, S.; Department of Computer Science and Engineering, UIT, Rajiv Gandhi Proudyogiki

Vishwavidyalaya, Bhopal, M.P , India; email:[email protected]

Copyright 2014 Elsevier B.V., All rights reserved.

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