Fingerprint Identification Based on Mopso in SVM

Article Preview

Abstract:

Abstract. The problem of fingerprint classification is discussed for many years. Support Vector Machine (SVM) is a traditional artificial intelligence algorithm developed for dealing classification problems. In this paper, we used the idea of multi-objective optimization to transform SVM into a new form, since the core concept of SVM is built up on a single optimization equation, and some parameters for this algorithm still need user to make tons of experiment to determine. Our algorithm has successfully proved that user do not need to make experiment to determine the penalty parameter C. NIST-4 database is used to assess our proposed algorithm. The experiment results show our method can get good classification results.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

810-817

Citation:

Online since:

December 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] L. A. Alexandre, A. C. Campilho and M. Kamel, On combining classifiers using sum and product rules, Patter Recogn. Lett. 22 (2001), pp.1283-1289.

DOI: 10.1016/s0167-8655(01)00073-3

Google Scholar

[2] Carlo R. Raquel, Prospero C. Naval, An effective use of crowding distance in multiobjective particle swarm optimization, In Proc. of Genetic and Evolutionary Conference (2005), ACM , pp.257-64.

DOI: 10.1145/1068009.1068047

Google Scholar

[3] Margarita Reyes-Sierra and Carlos A. Coello, Multi-Objective Particle Swarm Optimizers : A Survey of the State-of-the-Art, , International Journal of Computational Intelligence Research, Vol. 2, No. 3, (2006), pp.287-308.

DOI: 10.5019/j.ijcir.2006.68

Google Scholar

[4] Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. A fast elitis nondominated sorting genetic algorithm for multiobjective optimization: NSGA-II. In Proc. Parallel Problem Solving from Nature VI Conference, (2000), pp.849-858.

DOI: 10.1007/3-540-45356-3_83

Google Scholar

[5] Knowles, J. and Corne, D. Approximating the nondominated front using the Pareto archived evolutionary strategy, Evol. Computing, vol. 8, (2000), pp.149-172.

DOI: 10.1162/106365600568167

Google Scholar

[6] Van Veldhuizen, D. and Lamont, G. Multiobjective evolutionary algorithms research: A history and analysis. Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst. Technol., Wright-Patterson AFB, OH, Tech. 1998, Rep. TR-98-03.

Google Scholar

[7] Kennedy, J. and Eberhart , R. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks. IV, (1995), p.1942-(1948).

Google Scholar

[8] Coello, C., Pulido, G., and Salazar, M. Handling multiobjectives with particle swarm optimization. In IEEE Transactions on Evolutionary Computation, vol. 8, (2004), pp.256-279.

DOI: 10.1109/tevc.2004.826067

Google Scholar

[9] Parsopoulos, K. and Vrahatis, M. Particle swarm optimization method in multiobjective problems. In Proc. 2002 ACM Symp. Applied Computing (SAC' 2002), (2002), pp.603-607.

DOI: 10.1145/508791.508907

Google Scholar

[10] Li, X. et al. A nondomiated sorting particle swarm optimizer for multiobjective optimization. In Lecture Notes in Computer Science, vol. 2723, Proc. Genetic and Evolutionary Computation, GECCO 2003, Part I, (2003), pp.37-48.

DOI: 10.1007/3-540-45105-6_4

Google Scholar

[11] K. Deb, Amrit Pratap, Sameer Agarwal and T. Meyarivan, A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, (2002).

DOI: 10.1109/4235.996017

Google Scholar

[12] Ingo Mierswa, Controlling overfitting with multi-objective support vector machine, In Proceedings of the 9th annual conference on Genetic and evolutionary computation, (2007), pp.1830-1837.

DOI: 10.1145/1276958.1277323

Google Scholar

[13] Sen Wang; Yangsheng Wang; Fingerprint Enhancement in the Singular Point Area, Signal Prcessing Letters IEEE , Volume: 11, Issue: 1, (2004), pp.16-19.

DOI: 10.1109/lsp.2003.819351

Google Scholar

[14] Hsieh, Ching-tang; Hu, Chia-shing; A Fingerprint Identification System Based on Fuzzy Encoder and Neural Network, Tamkang Journal of Science and Engineering 11(4), (2008), pp.347-355.

Google Scholar