Performance analysis of grid & texture based feature vector for dynamic signature recognition

Abstract

Online signature recognition is one of the important behavioral biometric trait. This signature has information of x, y, z variations, pressure, azimuth of pen tip, pen tip altitude. This makes online handwritten signature based biometric system more accurate than the static ones. In this paper new set of features are proposed for online or dynamic signature recognition. These features were originally proposed for static systems and in this research they are modified for the dynamic signature based system. Grid & texture features based feature vector and their extraction mechanism is proposed here. The results indicate that the online system give better accuracy and convergence as compared to the static system.