Knowledge-based fingerprint post-processing

True minutiae extraction in fingerprint image is critical to the performance of an automated identification system. Generally, a set of endings and bifurcations (both called feature points) can be obtained by the thinning image from which the true minutiae of the fingerprint are extracted by using the rules based on the structure of ridges. However, considering some false and true minutiae have similar ridge structures in the thinning image, in a lot of cases, we have to explore their difference in the binary image or the original gray image. In this paper, we first define the different types of feature points and analyze the properties of their ridge structures in both thinning and binary images for the purpose of distinguishing the true and false minutiae. Based on the knowledge of these properties, a fingerprint post-processing approach is developed to eliminate the false minutiae and at the same time improve the thinning image for further application. Many experiments are performed and the results have shown the great effectiveness of the approach.