Verifying online signatures through an iterative device independent model

Abstract

Hand signatures are getting used from as early as we invented writing. In 3100 BC, we found examples of people using words and symbols to denote their identity. It has also been used as a method of identification. Modern society kept hand signatures for many purposes like the authentication of banking and real estate fields. The recent trend of working from home and business on the go created a necessity to bring the signature from paper to smartphone. Statistics also indicated that it is a user-preferred method of verification. In this paper, we proposed a novel method to verify online signatures using an iterative approach that is device independent. It will be helpful to bring the signatures from paper to smartphones. In this method, we have created a model per signatory, based on their behavioral pattern on each point based on time and distance from the start of the signature. We also considered the defference between the signatory’s own signatures while training. We worked with defferent derived datapoints like velocity, angular velocity etc. We have achieved 8% EER on the MCYT dataset and 20% EER on the Mobisig dataset.