Improved Offline Signature Verification Scheme Using Feature Point Extraction Method
Abstract
Signature verification is a technology that can solve security problems in our networked society. A new improved offline signature verification scheme is proposed which is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective is to reduce the two vital parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) which are normally used in any signature verification scheme. Comparative analysis has been made with standard existing schemes.
DOI: https://doi.org/10.3844/jcssp.2008.111.116
Copyright: © 2008 Debasish Jena, Banshidhar Majhi and Sanjay K. Jena. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Offline signature
- geometric centre
- feature point
- forgeries
- euclidean distance model
- FAR (False Acceptance Rate)
- FRR (False Rejection Rate)