Student Number : 9901877H
MSc Dissertation
School of Electrical and Information Engineering
Faculty of Engineering and the Built Environment / algorithm. The origins of handwriting idiosyncrasies and habituation are explained using systems
theory, and it is shown that the 2/3 power law governing biomechanics motion also applies to
handwriting. This leads to the conclusion that it is possible to derive handwriting velocity
profiles from a static image, and that a successful forgery of a signature is only possible in the
event of the forger being able to generate a signature using natural ballistic motion. It is also
shown that significant portion of the underlying dynamic system governing the generation of
handwritten signatures can be inferred by deriving time segmented transfer function models of
the x and y co-ordinate velocity profiles of a signature. The prototype algorithm consequently
developed uses x and y components of pen-tip velocity profiles (vx[n] and vy[n]) to create
signature representations based on autoregression-with-exogenous-input (ARX) models.
Verification is accomplished using a similarity measure based on the results of a k-step ahead
predictor and 5 complementary metrics. Using 350 signatures collected from 21 signers, the
system’s false acceptance (FAR) and false rejection (FRR) rates were 2.19% and 27.05%
respectively. This high FRR is a result of measurement inadequacies, and it is believed that the
algorithm’s FRR is approximately 18%.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/1514 |
| Date | 31 October 2006 |
| Creators | Gu, Yi |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 2406244 bytes, application/pdf, application/pdf |
Page generated in 0.002 seconds