A multiple feature-based offline handwritten signature verification system

Abstract

This paper proposes an efficient technique to develop an automated offline signature verification system that could help in crime prevention and biometric authentication systems. The technique proposed makes use of direction-based methods to compute a set of features that are taken together as a combination. The features include the geometric details of the different strokes that compose a signature and contours of the signature. It includes the two-step/three-step features, radical points, directions and transitions of strokes and contours, energy density and angles of strokes to name a few. A grid-based approach is applied to extract some of the features. Classification is done by using Support Vector Machine (SVM). Experiments are performed on a standard CEDAR database and a self-prepared database. The results speak for the efficiency of the proposed system that achieves accuracy level much better than many of the published works.