Signature Verification Using a Bayesian Approach

Authors:
Sargur N. Srihari;Kamal Kuzhinjedathu;Harish Srinivasan;Chen Huang;Danjun Pu
Affiliations:
Center of Excellence for Document Analysis and Recognition Department of Computer Science and Engineering, University at Buffalo, State University of New York,;Center of Excellence for Document Analysis and Recognition Department of Computer Science and Engineering, University at Buffalo, State University of New York,;Center of Excellence for Document Analysis and Recognition Department of Computer Science and Engineering, University at Buffalo, State University of New York,;Center of Excellence for Document Analysis and Recognition Department of Computer Science and Engineering, University at Buffalo, State University of New York,;Center of Excellence for Document Analysis and Recognition Department of Computer Science and Engineering, University at Buffalo, State University of New York,
Venue:
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Year:
2008

Citing 3
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Abstract

The fully Bayesian approach has been shown to be powerful in machine learning. This paper describes signature verification using a non-parametric Bayesian approach. Given sample(s) of Genuine signatures of an individual, the task of signature verification is a problem of classifying a questioned signature as Genuine or Forgery. The verification problem is a two step approach - (i)Enrollment: Genuine signature samples of an individual are provided. The method presented here maps from features space to distance space by comparing all the available genuine signature samples amongst themselves to obtain a distribution in distance space - "within person distribution". This distribution captures the variation and similarities that exist within a particular person's signature; (ii)Classification: The questioned signature to be classified, is then compared to each of the genuine signatures to obtain another distribution in distance space - "Questioned vs Known distribution". The two distributions are then compared using a new Bayesian similarity measure to test whether the samples in the distribution are from the same distribution(Genuine) or not(Forgery). The approach yields improved performance over other non-parametric non Bayesian approaches.