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2009 WRI World Congress on Computer Science and Information Engineering
A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
This paper presents an automatic off-line signature verification system that is built using several statistical techniques. The learning phase involves the use of Hidden Markov Modelling (HMM) technique to build a reference model for each local feature extracted from a set of signature samples of a particular user. The verification phase uses three layers of statistical techniques. The first layer involves the computation of the HMM-based log-likelihood probability match score. The second layer performs the mapping of this score into soft boundary ranges of acceptance or rejection through the use of z-score analysis and normalization function. Next Bayesian inference technique is used to arrive at the final decision of accepting or rejecting a given signature sample
Index Terms:
Hidden Markov Model (HMM), Bayesian Inference
Citation:
Sharifah Mumtazah Syed Ahmad, Asma Shakil, Masyura Ahmad Faudzi, Rina Md. Anwar, Mustafa Agil Muhamad Balbed, "A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System," csie, vol. 6, pp.6-11, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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