Supervised Neural Network and minimum distance features between singularities for fingerprint verification

This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutia´s. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutia points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase. The characterization efficiency of the proposed method is compared with the method based on the spatial coordinate position of fingerprint minutia´s. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. After that, the classification accuracy of the proposed approach is evaluated with Back Propagation Neural Network (BPNN). Extensive experiments prove that the Fingerprint classification based on a novel features and BPNN classifier give better results in fingerprint classification than several other features and methods.