Markov Model-Based Handwritten Signature Verification
Alan McCabe, Jarrod Trevathan · 2008
Biometric security devices are now permeating all facets of modern society. All manner of items including passports, driver's licences and laptops now incorporate some form of biometric data and/or authentication device. As handwritten signatures have long been considered the most natural method of verifying one's identity, it makes sense that pervasive computing environments try to capitalise on the use of automated Handwritten Signature Verification systems (HSV). This paper presents a HSV system that is based on a Hidden Markov Model (HMM) approach to representing and verifying the hand signature data. HMMs are naturally suited to modelling flowing entities such as signatures and speech. The resulting HSV system performs reasonably well with an overall error rate of 3.5% being reported in the best case experimental analysis.