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Off-line Handwritten Signature Verification using Artificial Neural Network Classifier

Handwritten signatures are used for major human identification procedures including money transfer dealings amongst other vitally important fields. Signatures can be perceived as a behavioral biometric and this paper evaluates the performance of an Error Back Propagation (EBP) Artificial Neural Network (ANN) for authenticating these. The work done has provided encouraging results and has re-confirmed the ability of Artificial Neural Networks to recognize patterns and in this case their skill to generalize. An efficient Static Signature Verification (SSV) system consists of rigorous preprocessing and feature extraction followed by a classifier. A database consisting of signatures from 6 individuals with 40 samples each, accounting to 240 samples in all, was used to evaluate the system performance and a verification rate of 94.27% was achieved.