Offline Signature Verification Using Support Vector Machine

This paper aims at developing a support vector machine for identity verification of offline signature based on the feature values in the database. A set of signature samples are collected from individuals and these signature samples are scanned in a gray scale scanner. These scanned signature images...

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Bibliographic Details
Published in2014 Fifth International Conference on Signal and Image Processing pp. 3 - 8
Main Authors Kruthi, C., Shet, Deepika C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2014
Subjects
EDH
Epsilon Intensive
Feature extraction
Handwriting recognition
Histograms
Image edge detection
Kernel
Kernel Perceptron
Large-Margin-Hyper plane
Offline signature verification
SMO
Support vector machines
Support Vectors
SVM
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Summary:This paper aims at developing a support vector machine for identity verification of offline signature based on the feature values in the database. A set of signature samples are collected from individuals and these signature samples are scanned in a gray scale scanner. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning and edge detection. From these pre-processed signatures, features such as centroid, centre of gravity, calculation of number of loops, horizontal and vertical profile and normalized area are extracted and stored in a database separately. The values from the database 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. The developed SVM is successfully tested against 336 signature samples and the classification error rate is less than 7.16% and this is found to be convincing.
DOI:10.1109/ICSIP.2014.5