Handwritten sigtures are considered as the most tural method of authenticating a person's identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten sigtures that are electronically captured via a stylus. This paper presents a method for verifying handwritten sigtures by using a NN architecture. Various static (e.g., height, slant, etc.) and dymic (e.g., velocity, pen tip pressure, etc.) sigture features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasobly well with an overall error rate of 3:3% being reported for the best case.
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