Automatic Detection of Secondary Creases in Fingerprints

Abstract

Human fingerprints comprise a series of whorls or ridges. In some special cases, these whorls are broken by so-called 'secondary creases': co-linear breaks across a sequence of adjacent ridges. A technique to automatically detect such creases in fingerprints is described. This technique utilizes a combination of spatial filtering and region-growing to identify the morphology of the locally fragmented fingerprint image. Regions are then thinned to form a skeletal model of the ridge structure. Creases are characterised by co-linear terminations on ridges and are isolated by analysing the Hough transform space derived from the ridge end points. Empirical results using both synthetic and real data are presented and discussed.