Finger vein recognition using Gabor filter and Support Vector Machine

Nowadays biometric identification systems are widely spread since the safety of those systems has been proven. They exhibit a large number of advantages when compared to other identification systems such as key and password that are subject to falsification and loss. Among biometric systems, finger vein recognition based on venous network has been considered recently in the literatures. This paper aims to present a finger vein recognition system using Support Vector Machine (SVM) based on a supervised training algorithm. The proposed system is divided in several phases, each performing a specific task. Two pre-processing schemes are employed in order to assess the efficiency in terms of recognition rate. Simulation results show that using Gabor filters in preprocessing for codifying the venous network and SVM for the classification can improve the recognition rate when compared to the existing methods.