Handwritten signature verification using complementary statistical models
McCabe, Alan, and Trevathan, Jarrod (2009) Handwritten signature verification using complementary statistical models. Journal of Computers, 4 (7). pp. 670-680.
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This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer's identity. This approach's novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any speCific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The results of several other experiments are also presented including using less reference Signatures, allOWing multiple signing attempts, zero- effort forgery attempts, providing visual feedback, and signing a password rather than a signature.
|Item Type:||Article (Refereed Research - C1)|
Reproduced with permission from Academy Publisher. © Academy Publisher.
|Keywords:||biometric security; fraud; artificial intelligence; privacy; algorithms; mathematical modelling|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 50%|
08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080303 Computer System Security @ 50%
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%|
|Deposited On:||03 Mar 2010 10:28|
|Last Modified:||02 Nov 2012 09:37|
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