JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 8    Date : August 2008

Neural Network-based Handwritten Signature Verification
Alan McCabe, Jarrod Trevathan, and Wayne Read
Page(s): 9-22
Full Text:
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Abstract
Handwritten signatures are considered as the most natural 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
signatures that are electronically captured via a stylus. This paper presents a method for verifying
handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and
dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the
NN. Several Network topologies are tested and their accuracy is compared. The resulting system
performs reasonably well with an overall error rate of 3:3% being reported for the best case.

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