A Novel Image Alignment and a Fast Efficient Localized Euclidean Distance Minutia Matching Algorithm for Fingerprint Recognition System

A fingerprint recognition system involves several steps. In such recognition systems, the matching of unequal number of minutia features is the most important and challenging step in fingerprint based bio-metrics recognition systems. In this paper, we used clustering based fingerprint image rotation algorithm, to improve the performance of the fingerprint recognition system and proposed a Localized Euclidean Distance Minutia Matching (LEDMM) algorithm for matching, which will give better results while comparing minutia sets of different sizes as well as in slightly different orientation during the matching process. The experimental results on the fingerprint image database demonstrate that the proposed methods can achieve much better minutia detection as well as better matching with improved performance in terms of accuracy.A fingerprint recognition system involves several steps. In such recognition systems, the matching of unequal number of minutia features is the most important and challenging step in fingerprint based bio-metrics recognition systems. In this paper, we used clustering based fingerprint image rotation algorithm, to improve the performance of the fingerprint recognition system and proposed a Localized Euclidean Distance Minutia Matching (LEDMM) algorithm for matching, which will give better results while comparing minutia sets of different sizes as well as in slightly different orientation during the matching process. The experimental results on the fingerprint image database demonstrate that the proposed methods can achieve much better minutia detection as well as better matching with improved performance in terms of accuracy.