Verification of dynamic signature using machine learning approach

This paper presents a novel approach for dynamic signature authentication based on the machine learning approach. In the proposed method, average values of features are taken into consideration for the verification. Here, seven different types (xandycoordinates, time stamp, pen ups and downs, azimuth, altitude and pressure) of features are used. The obtained extracted feature is learned into different classifiers. Different classifiers have been taken into consideration like random tree, Naive Bayes, random forest, J48, etc. These features are extracted from well-known SVC2004 dataset.