Offline Signature Verification based on Euclidean distance using Support Vector Machine

( Vol-2,Issue-8,August 2016 )

Author(s): Isha, Pooja, Varsha

Total View : 1208
Downloads : 164
Page No: 1262-1265
DOI:

Keywords:

Offline signature verification, Support Vectors, SVM, Euclidean Distance, SMO, EDH, Kernel Perceptron, Large-Margin-Hyper plane.

Abstract:

In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value.

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