Abstract
With the rapid improvement of information technology, security and authentication of individuals have become a greater significance. Iris recognition is one of the best solutions in providing unique authentication for individuals based on their IRIS structure. Iris normalization is meant to extract the iris region and represent it in the spatial domain, Daugman’s rubber sheet model is so far a standard and efficient method of implementing this process. In this paper, a low complex, simpler and improved version of rubber sheet model is proposed. The main aim of this method is to minimize the complex computations that were involved in the conventional rubber sheet model and to provide an equivalent performing approach with very less computations. Classification performance is evaluated with CASIA and IIT Delhi IRIS databases using SVM classifier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Seetharaman K, Ragupathy R (2012) Iris recognition based image authentication. Int J Comput Appl 44(7)
Daugman J (2002) How iris recognition works. In: Proceedings of 2002 international conference on image processing, vol 1
Wildes R (1997) Iris recognition: an emerging biometric technology. In: Proceedings of the IEEE, vol 85, no 9
Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S (1994) A system for automated iris recognition. Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp 121–128
Boles W, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4)
Lim S, Lee K, Byeon O, Kim T (2001) Efficient iris recognition through improvement of feature vector and classifier. ETRI J 23(2)
Noh S, Pae K, Lee C, Kim J (2002) Multi-resolution independent component analysis for iris identification. In: The 2002 international technical conference on circuits/systems, computers
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm
Amir AH, Reza P (2009) Efficient iris recognition through improvement of feature extraction and subset selection. Int J Comput Sci Inform Sec 2(1)
Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer-Verlag, New York
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Vapnik V (1998) Statistical Learning Theory. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Obul Kondareddy, R., Abraham David, B. (2019). Improved Normalization Approach for Iris Image Classification Using SVM. In: Saini, H., Singh, R., Patel, V., Santhi, K., Ranganayakulu, S. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 33. Springer, Singapore. https://doi.org/10.1007/978-981-10-8204-7_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-8204-7_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8203-0
Online ISBN: 978-981-10-8204-7
eBook Packages: EngineeringEngineering (R0)