A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.973
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
Hidden Markov Model (HMM), Bayesian Inference
A. Shakil, M. A. Faudzi, M. A. Balbed, S. M. Ahmad and R. M. Anwar, "A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 6-11.