Offline signature verification based on geometric feature extraction using artificial neural network

Signature verification is widely used for personal verification. But, it has an inevitable problem of getting exploited for forgery therefore an automatic signature recognition and verification system is required. Verification can be accomplished either Online or Offline based application. Offline systems work on the scanned image of a signature. Online systems use dynamic information like pressure, speed etc. of a signature during the time when the signature is made. In this paper, we present an offline signature verification technique based on geometric features. We have used six geometric features namely Area, Centroid, Standard deviation, Even pixels, Kurtosis and Skewness. In our technique first the preprocessing of a scanned signature image is done to isolate the signature and to remove noise. The system is trained using a database of signatures obtained from authenticated users. Then artificial neural network (ANN) is used in recognition and verification of signatures: genuine or forged, and efficiency is about 89.24% having threshold of 80%. Simulation results shows that the technique is robust and clearly differentiates between genuine and forgery signatures.