A NON-LINEAR FILTERING APPROACH TO RIDGE AND FURROW SEGMENTATION OF FINGERPRINTS

Abstract

Ridge and furrow segmentation or ridge extraction is an important processing step in automatic fingerprint identification; as its success simplifies the task of tracing the most distinguishing features of the print, the ridge ends and bifurcations. In this work a new method for ridge extraction in fingerprints is proposed. The method uses normalization, local histogram equalization, median filtering and global thresholding  to segment the fingerprint foreground into ridges and furrows. The result obtained shows that the new algorithm is robust to image noise. It is also less computationally demanding when compared with an earlier ridge detection scheme.