Bi-orthogonal Wavelet Based Comer Template Encoding Methodology

Issue: Vol.8 No.1

Authors:

Sherin Zafar (Jamia Hamdard University, New Delhi)

Deepa Mehta (Manav Rachna International University, Faridabad)

Keywords: Template Encoding, Bi-orthogonal Wavelets, Lifting Scheme.

Abstract:

For performing feature extraction of iris, one of the strong biometric feature, encoding is the most important operation. Template formation or encoding in the proposed methodology is performed through convolving the normalized iris pattern with bi-orthogonal wavelets 3.5. The 2D normalized  pattern is broken up into 1D-signals. The angular direction is taken rather than the radial which corresponds to columns of the normalized pattern, as the maximum independence occurs in the angular direction. Transformation of the segmented iris information into a normalized iris data is done using the biorthogonal tap. In the proposed method rather than utilizing the traditional Multi-Resolution Analysis (MRA) scheme, a novel lifting technique is explored for the construction of bi-orthogonal filters. The main advantage of this scheme over the classical construction methods is that it does not rely on the Fourier transform and results in faster implementation of wavelet transform.

References:

[1] Abhyankar,A. and Schuckers,S., 2010. Wavelet Based IrisRecognition for Robust Biometric System. International Journal of Computer Theory and Engineering, 2 (2). [Accessed April 2010].

[2] Boles,W.W. and Boashash,B., 1998. AHuman Identification Technique using Images of the Iris and Wavelet Transform. IEEE Transactions on Signal Processing, 46 (4).

[3] Calderbank, A.R. Daubechies, I.,Sweldens, W. andYeoB.L., 1998. Wavelet Transforms that Map Integers to Integers. Applied and Computation Harmonic Analysis (3), 332-369.

[4] Daubechies, I. and Sweldens, W., 1998. Factoring Wavelet Transforms into Lifting Steps. Journal of Fourier Analysis and Applications, 4 (3), 245-267.

[5] Daugman,J., 2002. How Iris Recognition Works. Proceedings of 2002 International Conference on Image Processing, 1. 

[6] Lim, S., Lee,K., Byeon,O. and Kim. T.,2001. Efficient Iris Recognition through Improvement of Feature Vector and Classifier. ETRI Journal, 23 (2).

[7] Lim, S., Yu, C. and Das, C., 2005. A Randomized Communication Scheme for Improving Energy Efficiency in Mobile Ad-hoc Networks. Proceedings of 25thInternational Conference on Distributed Computing Systems (ICDCS), 123-132.

[8] Masek,L., 2003. Recognition of Human Iris Patterns for Biometric Identification.University of Western Australia. Cheng, H., 2010. Genetic Algorithms with Immigrants Schemes for Dynamic Multicast Problems in Mobile Adhoc Networks. Engineering Applications of Artificial Intelligence Elsevier, 806-819.

[9] Panganiban,A., Linsangan,N. and Caluyo,F., 2011. WaveletBased Feature Extraction Algorithm for an Iris Recognition System. Journal of Information Processing Systems, 7 (3). [Accessed September 2011].

[10] Ritter, N., 1999. Location of the Pupil-Iris Border in SlitLamp Images of the Cornea.In:Proceedings of the International Conference on Image Analysis and Processing. IEEE International Symposium on Signal
Processing and Information Technology.

[11] Struc,V., Gajsek,R. and Pavasic,N., 2009. Principal Gabor Filters for Face Recognition 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, 1-6. [Accessed September 2009].

[12] Sweldens, W., 1995. The Lifting Scheme: A New Philosophy in Bi-Orthogonal Wavelet Constructions.
Wavelet Applications in Signal and Image Processing III, SPIE 2569, 68-79.

[13] Sweldens, W., 1997. The Lifting Scheme: A Construction of Second Generation Wavelets. SIAM Journal of Math Analysis, 29 (2), 511-546.

[14] Wildes, R., 1997. Iris Recognition: An Emerging Biometric Technology. In:Proceedings of the IEEE, 85 (9).

[15] Wildes, R.P., Asmuth, J.C., Green, G.L. and Hsu, H.C., 1994. A System for Automated Iris Recognition. In: Proceedings IEEE Workshop on Applications of Computer Vision, 121-128.

[16] Zafar Sherin, Soni.M.K., Beg M.M.S 2015. An Optimized Genetic Stowed Biometric Approach to Potent QOS in MANET. Procedia Computer ScienceVolume 62, 2015, Pages 410–418 (Elsevier)Proceedings of the 2015 International Conference on Soft Computing and Software Engineering (SCSE’15).

[17] Zafar Sherin, Soni.M.K. 2014. Biometric Stationed Authentication Protocol (BSAP) Inculcating Meta-Heuristic Genetic Algorithm . I.J. Modern Education and Computer Science, 28-35.

[18] Zafar Sherin, Soni.M.K. 2014. A Novel Crypt-Biometric Perception Algorithm to Protract Security in MANET. Genetic Algorithm . I.J. Computer Network and Information Security, 64-71.

[19] Zafar Sherin, Soni.M.K. 2014. Secure Routing in MANET through Crypt-Biometric Technique. Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA),713-720.

[20] Zafar Sherin, Soni.M.K., Beg M.M.S 2015. QOS Optimization in Networks through Meta-heuristic
Quartered Genetic Approach. ICSCTI,IEEE.