Neural network-based handwritten signature verification

McCabe, Alan, Trevathan, Jarrod, and Read, Wayne (2008) Neural network-based handwritten signature verification. Journal of Computers, 3 (8). pp. 9-22.

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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.

Item ID: 6559
Item Type: Article (Research - C1)
ISSN: 1796-203X
Keywords: biometrics; neural networks; prediction; type 1 and type 2; artificial intelligence; security
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Reproduced with permission from Academy Publisher. © Academy Publisher

Date Deposited: 30 Mar 2010 03:42
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