Using Duality and Hopfield Neural Network for Delaunay Triangulation Based Fingerprint Matching

In this paper we present a new method for fingerprint matching which is based on the calculation of Delaunay Triangulation (DT) of the minutiae set. The obtained DT is transformed to a set of points in the discretized space using duality. This translation results in a sampling method be acquiring which the system tolerates displacement and noise of the input image. Finally a Hopfield Neural Network (HNN) is used to learn the obtained pattern. Experimental results show a significant improvement in the false rejection rate over both the traditional DT-based approach and the direct HNN application.