Neural network based minutiae filtering in fingerprints
Dario Maio, Davide Maltoni · 2002
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.