Fingerprint Database Optimization Using Watershed Transformation Algorithm

Owoeye Kolade, Ajayi Adedoyin Olayinka, Ukorigho Ovie

Department of Mathematical Sciences, Ekiti State University, Ado Ekiti, Nigeria.

DOI: 10.4236/ojop.2014.34006                

Abstract

Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorithms have been used in the time past but there are still several challenges facing some current segmentation algorithms like computational efficiency. Another challenge is that segmentation procedure can be impractically slow, or requires extremely large amounts of memory. This paper addresses the challenges by employing watershed flooding algorithm on the fingerprint images so as to optimize the sizes of the databases. A pre-processing plug-in that implements this segmentation process is developed using Java. We showed its effectiveness by testing it on fingerprint image dataset and the entropy showed that the segmented images sizes were reduced.

Keywords

Watershed, Transformation, Fingerprint, Segmentation, Entrophy

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Das, S. Lecture Notes. IIT Madras, India. http://vplab.iitm.ac.in/courses/CV_DIP/PDF/lect-Segmen.pdf
[2]	Vincent, L. and Soille, P. (1991) Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583-598. http://dx.doi.org/10.1109/34.87344
[3]	Digabel, H. and Lantuejoul, C. (1978) Iterative Algorithms. Proceedings of the 2nd European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, 85-89.
[4]	Beucher, S. and Meyer, F. (1993) The Morphological Approach to Segmentation: The Watershed Transformation, Mathematical Morphology in Image Processing. Marcel Dekker Inc., New York, 433-481.
[5]	Acharjya, P.P. and Ghoshal, D. (2012) An Effective Human Fingerprint Segmentation Method Using Watershed Algorithm. International Journal of Computer Applications, 53. http://dx.doi.org/10.5120/8482-2422
[6]	Ulbsibiu, R. (2014) Watershed Segmentation. http://remus.ulbsibiu.ro/teaching/courses/docs/acs/Watershed%20Segmentation.doc
[7]	Haralick, R.M. and Shapiro, L.G. (1985) Image Segmentation Techniques. Computer Vision, Graphics and Image Processing, 29, 100-132. http://dx.doi.org/10.1016/s0734-189x(85)90153-7
[8]	Pal, N.R. and Pal, S.K. (1993) A Review on Image Segmentation Techniques. Pattern Recognition, 26, 1277-1294. http://dx.doi.org/10.1007/978-3-662-21817-4_10
[9]	Berthold, K. and Horn, P. (1986) Robot Vision. MIT Press, Cambridge.
[10]	Ross Beveridge, J., Griffith, J., Kohler, R.R., Hanson, A.R. and Rise-Man, E.M. (1989) Segmenting Images Using Localized Histograms and Region Merging. International Journal of Computer Vision, 2, 311-347. http://dx.doi.org/10.1007/bf00158168
[11]	Roerdink, J.B.T.M. and Meijster, A. (2001) The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae, 41, 187-228.
[12]	http://imagej.nih.gov/ij/