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A New Approach for the Fingerprint Classification Based On Gray-Level Co- Occurrence Matrix

Authors: Mehran Yazdi, Kazem Gheysari


In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.

Keywords: Biometrics, Gray Level Cooccurrence Matrix, fingerprint classification, regular texture representation

Digital Object Identifier (DOI):

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[1] Galton, F. "Finger prints". McMillan, London, 1892.
[2] Henry, E "Classification and uses of finger prints" Rutledge, London, 1900.
[3] Neil Yager, Adnan Amin "Fingerprint classification: a review," Springer-Verlag London, 2004.
[4] Jain A, Prabhakar S, and Hong L, "A multichannel approach to fingerprint classification". IEEE Trans Patt Anal Mach Intell, Vol. 21, No. 4, pp. 348-359, 1999.
[5] Jain A, Prabhakar S, Pankanti S, "Matching and classification: a case study in the fingerprint domain," Proceedings of the Indian National Science Academy, Vol. 67, No. 2, pp. 67-85, 2001.
[6] Jain A and Minut S, "Hierarchical kernel fitting for fingerprint classification and alignment," Proc ICPR, Vol. 2, pp. 469-473, 2002.
[7] Chang J and Fan K, "A new model for fingerprint classification by ridge distribution sequences," Patt Recog , 2002.
[8] Yao Y, Frasconi P, and Pontil M "Fingerprint classification with combinations of support vector machines," Proceedings of the 3rd International Conference on Audio and Video Based Biometric Person Authentication, Halmstad, Sweden, June 2001.
[9] R. M. Haralick, K. Shanmugan and J. Dinstein, "Textual features for image classification" IEEE Trans. Syst. Man. Cybern. Vol. SMC-3, pp. 610-621, 1973.
[10] R. M. Haralick,"Statistical and Structural Approaches to Texture," Proceedings of IEEE, Vol. 67, No. 5, pp. 768-804, May 1979.
[11] Kyuheon Kim, Seyoon Jeong, Byung Tae Chun, Jae Yeon Lee, Younglae Bae, "Efficient video images retrieval by using local cooccurrence matrix texture features and normalized correlation," Proceedings of the IEEE Region 10 Conference TENCON 99. Vol. 2, pp. 934-937, Sept. 1999.