Performance evaluation of variable and modified Gabor kernels for filtering of fingerprint images

Abstract

AFIS (Automated Fingerprint Identification System) is the most widely used identification system. But the most critical part in fingerprint identification is reliable extraction and matching of fingerprint features which depends on quality of input fingerprint image. But it is quite rare that the input fingerprint image will be of very good quality. So there is necessity of some fingerprint enhancement technique to enhance the features of fingerprints before extraction and matching. In this experiment, variable traditional gabor filter (TGF) and modified gabor filter (MGF) are compared in terms of performance, particularly for average and poor quality fingerprint images. Simulation results indicate that, as the quality of fingerprint image degrades, MGF performs more better than TGF in terms of reducing spurious minutiae.