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Comparative Analysis of Minutiae Based Fingerprint Matching Algorithms


Affiliations
1 School of Computing and Informatics, University of Nairobi, Kenya
 

Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the matching algorithm determines its effectives. This research aims at comparing two types of matching algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a data sets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya. There search reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy, algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.

Keywords

AFIS, ANN, Fingerprint Recognition, FMR, FAR.
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  • Comparative Analysis of Minutiae Based Fingerprint Matching Algorithms

Abstract Views: 363  |  PDF Views: 211

Authors

Silas KivutiNjeru
School of Computing and Informatics, University of Nairobi, Kenya
Robert Oboko
School of Computing and Informatics, University of Nairobi, Kenya

Abstract


Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the matching algorithm determines its effectives. This research aims at comparing two types of matching algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a data sets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya. There search reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy, algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.

Keywords


AFIS, ANN, Fingerprint Recognition, FMR, FAR.