Combinatorial Classification of Pixels for Ridge Extraction in a Gray-Scale Fingerprint Image

Automatic Fingerprint Identification Systems (AFIS) are usually based on minutiae matching. Minutiae are the terminations and bifurcations of the ridge lines in a fingerprint image. Detection of ridge lines from a noisy gray-scale fingerprint image is a challenging task. In this work, a novel combinatorial approach is proposed for classifying each pixel into one of the three classes (crest, valley, and slope) based on its gray-scale topographical relationship with its neighbors. A two-pass algorithm is developed for the pixel classification scheme. The ridge lines are then detected as formed by the thinned version of the crest pixels. The algorithm is robust, performs very well in the presence of noise, and has minimal dependence on thresholding. It has been tested on several fingerprint images in the NIST Special Database 14 and NIST Special Database 4, and is observed to produce good results both in terms of quality of solutions and CPU time.