Neural network based minutiae filtering in fingerprints

Minutiae correspond essentially to the terminations and bifurcations of fingerprint patterns. Since the quality of fingerprint images is often low, automatic minutiae detection is a very difficult task and the extraction algorithms produce a large number of false alarms. We present an approach to minutiae filtering based on a neural network. The minutiae neighborhoods extracted by the algorithm presented by us (1997) are normalized with respect to rotation and scale, and their dimensionality is reduced via a KL transform. A neural classifier, whose topology has been designed to exploit the minutiae duality, is employed to perform the neighborhoods classification. The filtering proposed, as confirmed by simulations, allows a significant improvement in the overall performance to be achieved.