Iris Authentication Using PSO

International Journal of Computer & Organization Trends (IJCOT)          
© 2012 by IJCOT Journal
Volume-2 Issue-1                           
Year of Publication : 2012
Authors :Mr. Logannathan.B, Dr. Marimuthu. 


Mr. Logannathan.B, Dr. Marimuthu. "Iris Authentication Using PSO" . International Journal of Computer & organization Trends (IJCOT), V2(1):1-5 Jan - Feb 2012, Published by Seventh Sense Research Group.

Abstract—The paper proposes a wavelet probabilistic neural network (WPNN) for iris biometric classifier. The WPNN combines wavelet neural network and probabilistic neural network for a new classifier model which will be able to improve the biometrics recognition accuracy as well as the global system performance. A simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the wavelet probabilistic neural network. In iris matching, the CASIA iris database is used and the experimental results show that the feasibility and performance of the proposed method.


[1] Jain A.,Murty M., and Flynn P., “Data clustering: A Review”, ACAComputing Survey, Vol.31, No.3, 1999.
[2] Chen G.,Jaradat S.,Banerjee N., Tanaka T.,KoM., and Zhang M., “Evaluation and Comparison of Clustering Algorithms in Analyzing ES Cell Gene Expression Data”, Statistica Sinica, Vol.12, pp.241-262, 2002.
[3] R.T.Ng and J.Han, “Efficient and Effective Clustering Methods for Spatial Data Mining”, Proc.20th Int’l Conf. Very large Databases, pp.144-155, Sept.1994.
[4] D.Pollard , “A Centeral Limit Theorem for k-means Clustering,” Annals of Probability, Vol.10, pp.919-926, 1982.
[5] S.Z.Selim and M.A.Ismail, “k-mean-type Algorithms:A Generalized Converegence Theorem and Characterization of Local Optimality”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 6, pp.81- 87 , 1984.
[6] T.Zhang , R. Ramakrishnan , and M.Livny , “BIRCH: A New Data Clustering Algorithm and Its Applications,” Data Mining and Knowledge Discovery, Vol.1 , no.2 , pp.141-182 , 1997.
[7] Babu, G. P. And Murty, M. N. 1993. A nearoptimal initial Seed value selection in K-means algorithm using a genetic algorithm.
[8] P. K. Agarwal, and N. H. Mustafa, “k-means projective clustering,” in Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of database systems, 2004, Paris, France.
[9] J. Handl, J. Knowles, and D. B. Kell, “Computational cluster validation in post-genomic data analysis,” Bioinformatics, 2005