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Fingerprint Recognition using Daubauchi Wavelet and Radial Basis Function Neural Network

P. Guhan, S. Purushothaman, R. Rajeswari

Abstract


Fingerprint is a unique facility which is present in human anatomy. The ups and downs of the curvature present in the finger among human are different. The curvature present among male and female are also different. In general, the image of a finger either a thumb or index finger is scanned by a compact fingerprint scanner with high resolution.The fingerprint scanned w412ill go through preprocessing followed by wavelet decomposition.This paper implements wavelet decomposition for extracting features of fingerprint images. Subsequently, at the 5th level decomposition, statistical features are computed from the coefficients of approximation and detail. These features are used to train the radial basis function (RBF) neural network for identifying fingerprints. Sample finger prints are taken from database from the internet resource. The fingerprints are decomposed using daubauchi wavelet 1(db1) into 5 levels. The coefficients of approximation at the 5thlevel are used for calculating statistical features. These statistical features are used for training the RBF network.

Keywords


Fingerprint, Daubauchi Wavelet, Subband Wavelet Coefficients, Approximation and Details of 5 Level Decomposition, Radial Basis Function (Rbf).

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References


AvinashPokhriyal, SushmaLehri, 2010, A New Method Of Fingerprint Authentication Using 2d Wavelets, Journal of Theoretical and Applied Information Technology, Vol.13, No.2, pp.131-138.

Chen S., and Billings S.A, 1992, Neural networks for nonlinear dynamic system modeling and identification, International Journal of Control, Vol.56, No.22, pp.319-349.

Cowan C.F.N., Chen S., Billings S.A., and Grant P.M,1990, Practical Identification of NARMAX models using radial basis functions, International Journal of Control, Vol.52, pp.1327-1350.

Grant P.M., Chen S., and Billings S.A, 1992, Recursive hybrid algorithm for nonlinear system identification using radial basis function networks, International Journal of Control, Vol.55, No.5, pp.1051-1070.

Kumar S.D.R., Raja K.B., Chhotaray R.K., and Pattanaik S., 2011, DWT Based Fingerprint Recognition using Non Minutiae Features, IJSCI International Journal of Computer Science Issues, Vol.8, Issue 2, No.7, pp.257-265.

LinlinShen and Alex Kot, 2009, A New Wavelet Domain Feature for Fingerprint Recognition, Biomedical Soft Computing and Human Sciences, Vol.14, No.1, pp.55-59.

Moody, J., and Darken, C., 1989, Fast learning in networks of locally-tuned processing units, Neural Computation, Vol. 1, pp. 281-294.

Robert, M., Sanner, and Slotine, J.E., 1991, Stable adaptive control and recursive identification using radial gaussian networks, IEEE Proceedings of the 30th Conference on Decision and Control, Brighton, England. pp. 2116–2123.

ShabanaTadvi and Mahesh Kolte, 2010, A Hybrid System for Fingerprint Identification, International Journal on Computer Science and Engineering, Vol.2, No.3, pp.767-771.

Shashi Kumar, D.R.,, Raja, K. B., Chhotaray, R. K., and SabyasachiPattanaik, 2011, DWT Based Fingerprint Recognition using Non Minutiae Features, International Journal of Computer Science Issues, Vol. 8, Issue 2, pp.257-265.

Thaiyalnayaki, K., Syed Abdul Karim, S., and VarshaParmar, P., 2010, Finger Print Recognition using Discrete Wavelet Transform, International Journal of Computer Applications, Vol.1,No. 24,pp.96-100.


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