An approach to fingerprint filter design
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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. In this work we present a new approach to minutiae filtering based on a Neural Network. The minutiae neighborhoods extracted by the algorithm presented in [1] are normalized with respect to rotation and scale, and their dimensionality is reduced via 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 our simulations, allows a significant improvement in the overall performance to be achieved.