Offline signature verification based on geometric feature extraction using artificial neural network
Journal Title: IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) - Year 2018, Vol 13, Issue 3
Abstract
The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic verification system because of the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. A great deal of work has been done in the area of offline signature verification over the past few decades. Verification can be performed either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper, we present a method for Offline Verification of signatures using a set of simple shape based geometric features. The features that are used are Area, Euler’s Number, Eccentricity, Standard deviation, Centroid, Skewness, Kurtosis and Orientation. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used in recognition and verification of signatures: genuine or forged, and efficiency is about 86.67% having threshold of 80%. Simulation results shows that the technique is robust and clearly differentiates between genuine and forgery signatures.
Authors and Affiliations
Sumedha Tanajirao Panchal, V. V. Yerigeri
Keywords
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- EP ID EP439204
- DOI 10.9790/2834-1303035359.
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