Fingerprint ridge allocation in direct gray-scale domain

Abstract

Ridges and ravines are the main components constituting a fingerprint. Traditional automatic fingerprint identification systems (AFIS) are based on minutiae matching techniques. The minutiae for fingerprint identification are defined by ridge termination and ridge bifurcation. Most AFIS perform ridge line following process to automatically detect minutiae based on binary or skeleton fingerprint image. For low-quality fingerprint images, the preprocessing stage of an AFIS produces redundant minutiae or even destroys real minutiae. The minutiae detection algorithms in direct gray-scale domain have been developed to overcome these problems. The first step of gray-scale minutiae detection algorithm is to determine ridge locations and then perform gray-scale ridge line following algorithm to extract minutiae. However, the existing gray-scale minutiae detection techniques can only work on partial fingerprint image due to the ignorance of image background. Moreover, the gray value variation inside a ridge also generates redundant ridge points. In this paper, we propose a novel method, based on gray-level histogram decomposition, to locate the ridge points in complete fingerprint images. By decomposing the gray-level histogram, redundant ridge points can be eliminated according to some statistical parameters. Experimental results demonstrate that the correct rate can be over 95% even applied to poor-quality fingerprint images.