A New Approach for Fingerprint Classification based on Minutiae Distribution

Authors: Jayant V Kulkarni, Jayadevan R, Suresh N Mali, Hemant K Abhyankar, Raghunath S Holambe

Abstract:

The paper describes a new approach for fingerprint classification, based on the distribution of local features (minute details or minutiae) of the fingerprints. The main advantage is that fingerprint classification provides an indexing scheme to facilitate efficient matching in a large fingerprint database. A set of rules based on heuristic approach has been proposed. The area around the core point is treated as the area of interest for extracting the minutiae features as there are substantial variations around the core point as compared to the areas away from the core point. The core point in a fingerprint has been located at a point where there is maximum curvature. The experimental results report an overall average accuracy of 86.57 % in fingerprint classification.

Keywords: Minutiae distribution, Minutiae, Classification, Orientation, Heuristic.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334019

References:

[1] Galton F., Finger prints, McMillan, London, 1892.
[2] Henry E., Classification and uses of finger prints, Routledge, London, 1900.
[3] Kowagoe M. and Tojo A., "Fingerprints Pattern Classification," Pattern Recognition, vol.17, pp. 295-303, 1984.
[4] Karu K., Jain A. K., "Fingerprint Classification," Pattern Recognition, vol. 29, no.3, pp. 389-404, 1996.
[5] Hong L., Jain A. K., "Classification of Fingerprint Images," in Proc.Scandinvan Conf. on Image Analysis (11th), 1999.
[6] Cho B. H., Kim J. S., Bae J. H., Bae I. G., and Yoo K. Y., " Core-Based Fingerprint Image Classification," in Proc. Int. Conf. on Pattern Recognition (15th), vol. 2, pp. 863-866, 2000.
[7] Jain A. K., and Minut S., "Hierarchical Kernel Fitting for Fingerprint Classification and Alignment," in Proc. Int. Conf. on Pattern Recognition (16th), vol. 2, pp. 469-473, 2002.
[8] Maio D., Maltoni D., "A Structural Approach to Fingerprint Classification," in Proc. Int. Conf. On Pattern Recognition (13th), 1996.
[9] Chappelli R., Lumini A., Maio D., and Maltoni D., "Fingerprint Classification by Directional Image Partitioning," IEEE Transactions on Pattern Analysis and machine Intelligence, vol. 21, no. 5 pp. 402-421, 1999.
[10] Senior A., "A Hidden Markov Model Fingerprint Classifier," in Proc. Asilomar Conf. on Signals Systems and Computers (31st), pp. 306- 310,1997.
[11] H.C.Lee and R E Gaensslen. Advances in Fingerprint Technology. Elsevier, New York 1991.
[12] L. Hong and A. K. Jain, "Classification of Fingerprint Images," 11th Scandinavian Conference on Image Analysis, June 7-11, Kangerlussuaq, Greenland, 1999.
[13] A Ravishankar Rao, "A Taxonomy for Texture Description and Identification", Springer Verlag, New York 1990.
[14] Jayadevan.R,Jayant V Kulkarni,Suresh N Mali,Hemant K Abhyankar "A new ridge Orientation Based Method of Computation for Feature Extraction from Fingerprint Images"13th World Enformatika Conference on Pattern Analysis May 26-28,Budapest,Hungary 2006.
[15] B.M. Mehtre, "Fingerprint image analysis for automatic identification", Machine Vision and Applications Vol 6, pp.124-139, 1993.
[16] Amengual, J. C., Juan, A., Prez, J. C., Prat, F., Sez, S., and Vilar, J. M. "Real-time minutiae extraction in fingerprint images". In Proc. of the 6th Int. Conf. on Image Processing and its Applications, pp. 871-875, July 1997.
[17] Q. Xiao and H. Raafat. "Fingerprint image post processing A combined statistical and structural approach", Pattern Recognition Vol. 24, No.10: pp. 985-992, 1991.