Volume 4 Number 7 (Jul. 2009)
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JCP 2009 Vol.4(7): 670-680 ISSN: 1796-203X
doi: 10.4304/jcp.4.7.670-680

Handwritten Signature Verification Using Complementary Statistical Models

Alan McCabe, Jarrod Trevathan
Discipline of Information Technology, James Cook University, Australia
Abstract—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.

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Cite: Alan McCabe, Jarrod Trevathan, "Handwritten Signature Verification Using Complementary Statistical Models," Journal of Computers vol. 4, no. 7, pp. 670-680, 2009.

General Information

ISSN: 1796-203X
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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