Impact of Algorithms for the Extraction of Minutiae Points in Fingerprint Biometrics
- 1 Government College of Engineering, India
- 2 Anna University of Technology, India
Copyright: © 2020 Sudha S. Ponnarasi and M. Rajaram. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: As the importance of automatic personal identification applications increases, biometrics particularly fingerprint identification is the most consistent and greatly acknowledged technique. A very important step in automatic fingerprint recognition system is to extract the minutiae points from the input fingerprint images automatically and quickly. Approach: Fingerprints from the database FVC2002 (DB1-a) is used for experimental purpose. The minutiae points from 100 fingerprints were detected. It is proposed to use Minutiae Detection using Crossing Numbers (MDCN) and Minutiae Detection using Midpoint Ridge Contour Method (MDMRCM). Finally the performance of minutiae extraction algorithms using the number of minutiae detected in both the cases were analysed. Results: The result shows that the avearge performance of MDCN method for minutiae points detection is 88% and for MDMRCM method is 92%. Conclusion: The performance of MDMRCM is better than MDCN method. MDMRCM method extract more minutiae points than MDCN method. It consumes lesser time to get the output and the false minutiae points were not detected. And hence MDMRCM method is considered to be a superior than MDCN method.
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- artificial neural network
- minutiae extraction
- crossing numbers
- midpoint ridge contour