ISSN : 1796-203X
Volume : 4    Issue : 7    Date : July 2009

Handwritten Signature Verification Using Complementary Statistical Models
Alan McCabe and Jarrod Trevathan
Page(s): 670-680
Full Text:
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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.

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