Online Signature Verification Based on Biometric Features

Abstract

Since current signatures are generally not verified carefully, frauds by forging others signature always happen. This paper tried to authenticate user automatically with electronic signatures on mobile device. We collected coordinates, pressure, contact area and other biometric data when users sign their name on touch screen smart phone. Then we used four different classification algorithms, Support Vector Machine, Logistic Regression, AdaBoost and Random Forest to build a specific signature verification model for each user, and compared the verification accuracy of these algorithms. The experimental result on 42 persons' dataset shows that these four algorithms have satisfactory performance on Chinese signature verification, and Adaboost has the best performance with error rate of 2.375%.