A fingerprint verification algorithm using the smallest minimum sum of closest Euclidean distance

In this thesis, a Euclidean distance based minutia matching algorithm is proposed to improve the matching accuracy in a fingerprint verification system. This algorithm extracts matched minutia pairs from input and template fingerprints by using the smallest minimum sum of closest Euclidean distance (SMSCED), corresponding rotation angle and empirically chosen statistical threshold values. Instead of using the minutia type and orientation angle, which are widely employed in existing algorithms, the proposed algorithm uses only the minutia location to reduce the effect of non-linear distortion. Experimental results show that the proposed method has higher accuracy with improved verification and rejection rates.