Detection of Virtual Core Point of a Fingerprint: A New Approach
Sarnali Basak1, Md. Imdadul Islam2, M. R. Amin3
1Sarnali Basak, Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.
2Md. Imdadul Islam, Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.
3M. R. Amin, Department of Electronics and Communications Engineering, East West University, Dhaka, Bangladesh.
Manuscript received on April 11, 2012. | Revised Manuscript received on April 14, 2012. | Manuscript published on May 05, 2012. | PP: 236-239 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0568042212/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In a fingerprint the profile of ridges are flowed by ridge orientation curves. The slope of each point of a ridge orientation curve varies with the radius of curvature of the line. The change in gradient will attain its maximum value when the curve changes its slope from positive to negative or vice versa which occurs on immediate left and right of maxima or minima point. Every ridge on a fingerprint will provide such point of maximum gradient and the mean value of those points is considered as the virtual core point. This paper presents a new model to determine the virtual core point based on changed in gradient of maxima and minima points, so that this core point is considered to be the reference point to select the region of interest (ROI) of a fingerprint for further processing. The results of the paper show that, the proposed method can provide the virtual core point from different types of fingerprint very efficiently and consequently simplifies the fingerprint recognition system.
Keywords: Change in gradient, maxima and minima points, non-minutia and minutia based detection, ridge orientation, ROI.