Nearest Neighbor Minutia Quadruplets based Fingerprint Matching with Reduced Time and Space Complexity

The fingerprint biometric is often used as the primary source of person authentication in a large population person identity system because fingerprints have unique properties like distinctiveness and persistence. However, the large volumes of fingerprint data may lead to the scalability issues which are to be addressed in the context of memory and computational complexity. In this paper, an attempt is made to develop an efficient fingerprint matching algorithm using nearest neighbor minutia quadruplets ( NNMQ). These minutia quadruplets are both rotation and translation invariant. Experimental results demonstrate that the proposed fingerprint matching algorithm achieves the reduced space and time complexities with the publicly available standard fingerprint benchmark databases FVC ongoing, FVC2000 and FVC2004.