Online finger-knuckle-print verification for personal authentication

Abstract

Biometric based personal authentication is an effective method for automatically recognizing, with a high confidence, a person's identity. By observing that the texture pattern produced by bending the finger knuckle is highly distinctive, in this paper we present a new biometric authentication system using finger-knuckle-print (FKP) imaging. A specific data acquisition device is constructed to capture the FKP images, and then an efficient FKP recognition algorithm is presented to process the acquired data in real time. The local convex direction map of the FKP image is extracted based on which a local coordinate system is established to align the images and a region of interest is cropped for feature extraction. For matching two FKPs, a feature extraction scheme, which combines orientation and magnitude information extracted by Gabor filtering is proposed. An FKP database, which consists of 7920 images from 660 different fingers, is established to verify the efficacy of the proposed system and promising results are obtained. Compared with the other existing finger-back surface based biometric systems, the proposed FKP system achieves much higher recognition rate and it works in real time. It provides a practical solution to finger-back surface based biometric systems and has great potentials for commercial applications.