Generative models for fingerprint individuality using ridge models

Generative models of pattern individuality attempt to learn the distribution of observed quantitative features to determine the probability of two random patterns being the same. Considering fingerprint patterns, Gaussian distributions have been previously used for minutiae location and von-Mises distributions for minutiae orientation so as to determine the probability of random correspondence (PRC) between two fingerprints. Motivated by the fact that ridges have not been modeled in generative models and the benefits from ridge points in fingerprint matching, ridge information is incorporated into the generative model by using the distribution for ridge point location and orientation. The proposed model offers a more accurate fingerprint representation from which more reliable PRCs can be computed. Based on parameters estimated from fingerprint databases, PRCs using ridge information are seen to be much smaller than PRCs computed with only minutiae.