Invariant and reduced features for Fingerprint Characterization

In this paper, we propose a new method for fingerprint identification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutiae´s. The main advantage of the new method is the reduced number of features vectors used to characterize fingerprint, compared with the classic characterization method based on the spatial coordinate position of bifurcation minutiae points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase of image fingerprints. Whatever the degree of fingerprint rotation, the extraction features used to characterize the fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate position of fingerprint minutiae. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. Extensive experiments prove that the Fingerprint Characterization based on the Euclidean distance between the center point and their nearest neighbor bifurcation minutiae´s gives better results in fingerprint classification than several other features.