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Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques
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 Title & Authors
Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques
Bae, Jong-Soo; Oh, Sung-Kwun; Kim, Hyun-Ki;
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In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.
RBFNN;fingerprint;identification;pattern classifier;
 Cited by
Moon, J. H. et al. Study on Performance Evaluation for Biometric Verification System. Korean Institute of Information Scientists and Engineers, 19.7: 60-70, 2001.

Watson, C. I., G. T. Candela, and P. J. Grother. "Comparison of fft fingerprint filtering methods for neural network classification." NISTIR. 1994.

Chikkerur, Sharat, Chaohang Wu, and Venu Govindaraju. "A systematic approach for feature extraction in fingerprint images." Biometric Authentication. Springer Berlin Heidelberg, 344-350, 2004.

L. Hong, R. Bolle, A. Jain, "On-Line Fingerprint Verification," IEEE Trans. On PAMI, Vol. 19, No. 4, april 1997.

L. C. Jain, U. Halici, I. Hayashi, S. B. Lee, S, Tsutsui, Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC Press, 1999.

L.Hong, Y.Wan, A.Jain, "Fingerprint Image Enhancement: Algorithm and Performance Evaluation," IEEE Trans. On PAMI, Vol. 20, No. 8, august 1998.

M. D. Garris, C. I. Watson, R. M. McCabe, C. L. Wilson, User's Guide to NIST Fingerprint Image Software(NFIS), NISTIR 6813, NIST.

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, "Direct least-squares fitting of ellipses", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 476-480, 1999. crossref(new window)

R. C. Gonzalez, R. E. Woods, Digital Image Processing, Addison-Wesley, pp. 491-494, 1992.

C. Y. Cho, S. H. Kim, "Bilateral Symmetry Average Method for Robust Face Detection against Illumination Variation", journal of korea contents association, Vol.6, No. 12, pp. 21-28, 2006.

W. D. Kim, S. K. Oh, H. S. Park, and M. H. Son, "Identification Methodology of FCM-based Fuzzy Model Using Particle Swarm Optimization", The transaction of the Korean institute of electrical engineers 60(1), pp. 184-192, 2011 crossref(new window)

Kim Hyun-soo, "Secret sharing method between authorized persons using fingerprint identification algorithm cryptography", Master's Thesis, Dankook University, Yongin, Republic of Korea, 2003.

Shim Hyun Bo, "A Fingerprint Classification and Recognition using Gabor Feature Value", Ph.D, Myongji University, Yongin, Republic of Korea, 2009.

Jeong Hye Uk, "A Study on Fingerprint Classification Based on Ridges and Region Patterns Applicable to Various Quality and States", Ph.D, Sungkyunkwan University, Suwon, Republic of Korea, 2013.