Signature Analysis for Forgery Detection

Abstract

Forgery of signature has become very common, and the need for identification and verification is vital in security and resource access control. There are three types of forgery: random forgery, simple or casual forgery, expert or skilled or simulated forgery. The main aim of signature verification is to extract the characteristics of the signature and determine whether it is genuine or forgery. There are two types of signature verification: static or offline and dynamic or online. In our proposed solution, we use offline signature analysis for forgery detection which is carried out by first acquiring the signature and then using image pre-processing techniques to enhance the image. Feature extraction algorithms are further used to extract the relevant features. These features are used as input parameters to the machine learning algorithm which analyses the signature and detects for forgery. Performance evaluation is then carried out to check the accuracy of the output.