A novel method for fingerprint verification that approaches the problem as a two-class pattern recognition problem

We present a system for fingerprint verification that approaches the problem as a two-class pattern recognition problem. The distances of the test fingerprint to the reference fingerprints are normalized by the corresponding mean values obtained from the reference set, to form a five-dimensional feature vector. This feature vector is then projected onto a one-dimensional Karhunen-Loeve space and then classified into one of the two classes (genuine or impostor).