Finger-Knuckle-Print ROI Extraction using Curvature Gabor Filter for Human Authentication

Biometric based human recognition is a most obvious method for automatically resolving personal identity with high reliability. In this paper we present a novel finger-knuckle-print ROI extraction algorithm. The basic Gabor filter is modified to Curvature Gabor Filter (CGF) to obtain central knuckle line and central knuckle point which are further used to extract FKP ROI image. Largest public FKP database is used for testing which consists of 7,920 images collected from 660 different fingers. The results has been compared with the only other existing Convex Direction Coding (CDC) ROI extraction algorithm. It has been observed that the proposed algorithm achieves better performance with EER drop percentage more than 20% in all experiments. This suggests that the proposed CGF algorithm has been extracting ROI more consistently then CDC and hence can facilitates any finger-knuckle-print based biometric systems.