Extended clique models: A new matching strategy for fingerprint recognition

Establishing correspondences between minutiae sets is a fundamental issue in fingerprint recognition. This paper proposes a graph matching strategy. It addresses this problem using two extended clique models: the local clique model (LCM) and the global quasi-clique model (GQCM). First, minutia matching is formulated as maximal clique or quasi-clique detection. Similarities and compatibilities between minutia pairs are integrated and encoded into the correspondence graph. Correct matched minutia pairs then correspond to those strongly connected subgraphs, like the maximal clique or the maximal quasi-clique. Second, a two-level matching algorithm is proposed. In the local level, a novel similarity calculation of minutia topologic structures is proposed. It calculates similarities of minutia structures based on LCM and can be solved by maximal clique detection. In the global level, a novel similarity calculation of entire minutia sets is proposed. It calculates entire fingerprint similarity based on GQCM and can be solved by spectral correspondence techniques. Proposed method has stronger description ability and better robustness to noise and nonlinearity. Experiments conducted on FVC 2002 and 2004 databases demonstrate the effectiveness and the efficiency.