Feature Analysis in Online Signature Verification on Digital Whiteboard : An analysis on the performance of handwritten signature authentication using local and global features with Hidden Markov models

Abstract: The usage of signatures for authentication is widely accepted, and remains one of the most familiar biometric in our society. Efforts to digitalise and automate the verification of these signatures are hot topics in the field of Machine Learning, and a plethora of different tools and methods have been developed and adapted for this purpose. The intention of this report is to study the authentication of handwritten signatures on digital whiteboards, and how to most effectively set up a dual verification system based on Hidden Markov models (HMMs) and global aggregate features such as average speed. The aim is to gauge which features are Suitable for determining that a signature is in fact genuine Suitable for rejecting forgeries Unsuitable for gauging the authenticity of a signature all together In addition, we take a look at the configuration of the HMMs themselves, in order to find good configurations for The number of components used in the model What type of covariance to use The best threshold to draw the line between a genuine signature and a forgery For the research, we collected a total of 200 signatures and 400 forgeries, gathered from 10 different people on digital whiteboards. We concluded that the best configurations of our HMMs had 11 components, used a full covariance model, and observed about five features, where pressure, angle and speed were the most important. Among the global features, we discarded 11 out of 35 due to either strong correlation with other features, or contained too little discriminatory information. The strongest global features were the ones pertaining to speed, acceleration, direction, and curvature. Using the combined verification we obtained an EER of 7 %, which is in the typical range of contemporary studies. We conclude that the best way to combine global feature verification with local HMM verification is to perform both separately, and only accept signatures that are admissible by both, with a tolerance level for the global and local verifications of 1.2 and 2.5 standard deviations, respectively.