Fingerprint minutiae extraction from skeletonized binary images

Abstract

Fingerprint comparison is usually based on minutiae matching. The minutiae considered in automatic identification systems are normally ridge bifurcations and terminations. In this paper we present a set of algorithms for the extraction of fingerprint minutiae from skeletonized binary images. The goal of the present work is the extraction of the real 40–60 minutiae of a fingerprint image from the 2000–3000 contained in typical skeletonized and binarized images. Besides classical methodologies for minutiae filtering, a new approach is proposed for bridge cleaning based on ridge positions instead of classical methods based on directional maps. Finally, two novel criteria and related algorithms are introduced for validating the endpoints and bifurcations. Statistical analysis of the results obtained by the proposed approach shows efficient reduction of spurious minutiae. The use of the fingerprint minutiae extraction algorithms has also been considered in a fingerprint identification system in terms of timing and false reject or acceptance rates. The presented minutiae extraction algorithm performs correctly in dirty areas and on the background as well, making computationally expensive segmentation algorithms unnecessary. The results are confirmed by visual inspections of validated minutiae of the NIST sdb 4 reference fingerprint image database.