Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(315.66KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
The Biometric Algorithm based on Fusion of DWT Frequency Components of Enhanced Iris Image
Rangaswamy Y, Raja K B
Pages - 22 - 37     |    Revised - 31-03-2016     |    Published - 30-04-2016
Volume - 10   Issue - 1    |    Publication Date - April 2016  Table of Contents
MORE INFORMATION
KEYWORDS
Biometrics, Iris Recognition, DWT, Fusion, HE, AHE.
ABSTRACT
The biometrics are used to authenticate a person effectively compared to conventional methods of identification. In this paper we propose the biometric algorithm based on fusion of Discrete Wavelet Transform(DWT) frequency components of enhanced iris image.The iris template is extracted from an eye image by considering horizontal pixels in an iris part.The iris template contrast is enhanced using Adaptive Histogram Equalization (AHE) and Histogram Equalization (HE).The DWT is applied on enhanced iris template.The features are formed by straight line fusion of low and high frequency coefficients of DWT.The Euclidian distance is used to compare final test features with database features. It is observed that the performance parameters are better in the case of proposed algorithm compared to existing algorithms.
1 CiteSeerX 
2 refSeek 
3 Scribd 
4 SlideShare 
5 PdfSR 
1 J. Daugman, “How Iris Recognition Works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, pp.21-30, 2004.
2 L. Ma, T. Tan, Y. Wang and D. Zhang, “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Transactions on Image Processing, vol. 13, no. 6, pp. 739–750, 2004.
3 Ahmad Poursaberi and Babak N. Araabi , “A Novel Iris Recognition System using Morphological Edge detector and Wavelet Phase Features,” International Journal on Graphics, Vision and Image Processing,vol. 23, no. 2, pp.1-7, 2005.
4 C R Prashant, Shashikumar B R, K B Raja, K R Venugopal and L M. Patnaik, “High Security Human Recognition System using Iris Images,” International Journal of Recent Trends in Engineering, vol. 1, no. 1, pp 647-652, May 2009.
5 Mrinalini I R, Pratusha B P, Manikantan K and Ramachandran S “Enhance Iris Recognition using Discrete Cosine Transform and Radon Transform,” IEEE International Conference on Electronics and Communication Sustems pp. 1-6, 2015
6 Gagan R. and Lalitha S. “Elliptical Sector Based DCT Feature Extraction for Iris Recognition,” IEEE International Conference on Electrical, Computer and Communication Technologies, pp.1-5, 2015.
7 Radu P, Sirlantizis K, Howells W G, Hoque S and Deravi F. “Optimizing 2D Gabor Filters for Iris Recognition,” IEEE International Conference on Emerging Security Technologies, pp. 47-50. 2013.
8 Albadarneh A. Albadarneh I. and Alqatawna J. “Iris Recognition System for Secure Authentication based on Texture and Shape features,” IEEE International Conference on Applied Electrical Engineering and Computing Technologies, pp. 1-6, 2015.
9 Nigam A, Krishna V, Bendale A and Gupta P. “Iris Recognition using Block Local Binary Patterns and Relational Measures,” IEEE International Conference on Biometrics, pp. 1-6, 2014.
10 Isnanto R R, “Iris Recognition Analysis using Biorthogonal Wavelet Transform for Feature Extraction,” IEEE International Conference on Information Technology, Computer and Electrical Engineering, pp. 183-187, 2014.
11 Shashi Kumar D R, K B Raja, R K Chhootaray and Sabyasachi Pattnaik, “PCA based Iris Recognition using DWT,” International Journal Computer Technology and Applications, vol. 2, no. 4, pp. 884-893, 2011.
12 Umer S, Dhara B C, “A Fast Iris Localization using Inversion Transform and Restricted Circular Hough Transform,” IEEE International Conference on Advances in Pattern Recognition, pp.1-6 , 2015.
13 Podder P, Khan T Z, Khan M H, Rahman M M, Ahmed R and Rahaman M S, “ An Efficient Iris Segmentation Model based on Eyelids and Eyelashes Detection in Iris Recognition System,” IEEE International Conference on Computer Communication and Informatics, pp. 1-7, 2015.
14 Yongqiang LI , “Iris Recognition Algorithm based on MMC-SPP,” International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2, pp. 1-10, 2015.
15 http://www.sinobiometrics.com, CASIA Iris Image Database.
16 http://www.springerimages.com, Springer Analysis of CASIA Database.
17 Sharma D. P. “Intensity Transformation using Contrast Limited Adaptive Histogram Equalization,” International Journal of Engineering Research,vol. 2,no. 4,pp. 282-285, 2013.
18 Rafael C. Gonzalez and Richard E.Woods Digital Image Processing, Prentice Hall, Second Edition, 2002.
19 Mallat S “A Theory for Multiresolution Signal Decomposition: The wavelet Representation,” IEEE Transaction of Pattern Analysis and Machine Intelligence,vol. 11, pp. 674-693,1989.
20 Chun-Wei Tan and Ajay Kumar, “Accurate Iris Recognition at a distance using Stabilized Iris Encoding and Zernike Moments Phase Features,” IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3962-3974, 2014.
21 Sheela S V and Abhinand P “Iris Detection for Gaze Tracking Using Video Frames,” IEEE International Conference on Advance Computing, pp. 629-633, 2015.
22 Dong W, Sun Z and Tan T “Iris Matching based on Personalized Weight Map,” IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 33, no. 9, pp. 1744–1757, 2011.
23 C.W. Tan and A. Kumar, “Towards Online Iris and Periocular Recognition under Relaxed Imaging Constraints,” IEEE Transactions on Image Processing vol. 22, no. 9, pp. 3751-3765, 2013.
24 Khary Popplewell, Kaushik Roy, Foysal Ahmad, and Joseph Shelton, “Multispectral Iris Recognition Utilizing HoughTransform and Modified LBP,” IEEE International Conference on Systems, Man, and Cybernetics, pp. 1396-1399, 2014.
Dr. Rangaswamy Y
Dept of ECE, Alpha College of Engineering - India
Mr. Raja K B
University Visvesvaraya college of Engineering, Bangalore University - India
raja_kb@yahoo.com