Thesis (MSc)--University of Stellenbosch, 2002. / ENGLISH ABSTRACT: The science of signature verification is concerned with identifying individuals by their handwritten signatures. It is assumed that the signature as such is a unique feature amongst individuals and the creation thereof requires a substantial amount of hidden information which makes it difficult for another individual to reproduce the signature. Modern technology has produced devices which are able to capture information about the signing process beyond what is visible to the naked eye. A dynamic signature verification system is concerned with utilizing not only visible, i.e. shape related information but also invisible, hidden dynamical characteristics of signatures. These signature characteristics need to be subjected to analysis and modelling in order to automate use of signatures as an identification metric. We investigate the applicability of hidden Markov models to the problem of modelling signature characteristics and test their ability to distinguish between authentic signatures and forgeries. / AFRIKAANSE OPSOMMING: Die wetenskap van handtekeningverifikasie is gemoeid met die identifisering van individue deur gebruik te maak van hulle persoonlike handtekening. Dit berus op die aanname dat 'n handtekening as sulks uniek is tot elke individu en die generering daarvan 'n genoeg mate van verskuilde inligting bevat om die duplisering daarvan moeilik te maak vir 'n ander individu. Moderne tegnologie het toestelle tevoorskyn gebring wat die opname van eienskappe van die handtekeningproses buite die bestek van visuele waarneming moontlik maak. Dinamiese handtekeningverifikasie is gemoeid met die gebruik nie alleen van die sigbare manefestering van 'n handtekening nie, maar ook van die verskuilde dinamiese inligting daarvan om dit sodoende 'n lewensvatbare tegniek vir die identifikasie van individue te maak. Hierdie sigbare en onsigbare eienskappe moet aan analise en modellering onderwerp word in die proses van outomatisering van persoonidentifikasie deur handtekeninge. Ons ondersoek die toepasbaarheid van verskuilde Markov-modelle tot die modelleringsprobleem van handtekeningkarakteristieke en toets die vermoë daarvan om te onderskei tussen egte en vervalste handtekeninge.

Philosophiae Doctor - PhD / In this thesis, we explore dynamic signature verification systems. Unlike other signature models, we use genuine signatures in this project as they are more appropriate in real world applications. Signature verification systems are typical examples of biometric devices that use physical and behavioral characteristics to verify that a person really is who he or she claims to be. Other popular biometric examples include fingerprint scanners and hand geometry devices. Hand written signatures have been used for some time to endorse financial transactions and legal contracts although little or no verification of signatures is done. This sets it apart from the other biometrics as it is well accepted method of authentication. Until more recently, only hidden Markov models were used for model construction. Ongoing research on signature verification has revealed that more accurate results can be achieved by combining results of multiple models. We also proposed to use combinations of multiple single variate models instead of single multi variate models which are currently being adapted by many systems. Apart from these, the proposed system is an attractive way for making financial transactions more secure and authenticate electronic documents as it can be easily integrated into existing transaction procedures and electronic communications