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Journal of Information Science and Engineering, Vol. 25 No. 6, pp. 1955-1962 (November 2009)

Fingerprint Classification in DCT Domain using RBF Neural Networks*

CONG JIN AND PING JIN+
Department of Computer Science
Central China Normal University
Wuhan, 430079 P.R. China
+Price Quota Center
CNPC Huabei Petroleum
Hebei, 062552 P.R. China

Fingerprint classification is a fundamental method for the identification of people. Fingerprint classification is based on the immutability and the individuality of fingerprint. Because of the large collections of fingerprints and recent advances in computer technology, there has been increasing interest in automatic classification of fingerprint. In this paper, an efficient method for fingerprint classification based on the discrete cosine transform (DCT), fuzzy c-means clustering (FCM), the Fishers linear discriminant (FLD) and radial basis function (RBF) neural networks is proposed. Experimental results show that the proposed method achieves excellent performance with high correctly recognition rate, very low reject rate, and very less running time.

Keywords: fingerprint classification, RBF neural networks, FLD, FCM, DCT

Full Text () Retrieve PDF document (200911_18.pdf)

Received November 28, 2007; revised April 1, 2008 & December 31, 2008; accepted January 8, 2009.
Communicated by Pau-Choo Chung.
* This work was supported by the Natural Science Foundation of Hubei (China) and Grant No. 2008CDB349.