In today’s society signatures are the most accepted form of identity verification. However, they have the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. This thesis implements and tests current approaches to off-line signature verification with the goal of determining the most beneficial techniques that are available. This investigation will also introduce novel techniques that are shown to significantly boost the achieved classification accuracy for both person-dependent (one-class training) and person-independent (two-class training) signature verification learning strategies. The findings presented in this thesis show that many common techniques do not always give any significant advantage and in some cases they actually detract from the classification accuracy. Using the techniques that are proven to be most beneficial, an effective approach to signature verification is constructed, which achieves approximately 90% and 91% on the standard CEDAR and GPDS signature datasets respectively. These results are significantly better than the majority of results that have been previously published. Additionally, this approach is shown to remain relatively stable when a minimal number of training signatures are used, representing feasibility for real-world situations.

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Thesis (PhD)--Stellenbosch University, 2015 / AFRIKAANSE OPSOMMING : In hierdie verhandeling stel ons 'n nuwe strategie vir outomatiese handtekening-verifikasie voor. Die voorgestelde raamwerk gebruik 'n skrywer-onafhanklike benadering tot handtekening- modellering en is dus in staat om bevraagtekende handtekeninge, wat aan enige skrywer behoort, te bekragtig, op voorwaarde dat minstens een outentieke voorbeeld vir vergelykingsdoeleindes beskikbaar is. Ons ondersoek die tradisionele statiese geval (waarin 'n bestaande pen-op-papier handtekening vanuit 'n versyferde dokument onttrek word), asook die toenemend gewilde dinamiese geval (waarin handtekeningdata outomaties tydens ondertekening m.b.v. gespesialiseerde elektroniese hardeware bekom word). Die statiese kenmerk-onttrekkingstegniek behels die berekening van verskeie diskrete Radontransform (DRT) projeksies, terwyl dinamiese handtekeninge deur verskeie ruimtelike en temporele funksie-kenmerke in die kenmerkruimte voorgestel word. Ten einde skryweronafhanklike handtekening-ontleding te bewerkstellig, word hierdie kenmerkstelle na 'n verskil-gebaseerde voorstelling d.m.v. 'n geskikte digotomie-transformasie omgeskakel. Die klassikasietegnieke, wat vir handtekeking-modellering en -verifikasie gebruik word, sluit kwadratiese diskriminant-analise (KDA) en steunvektormasjiene (SVMe) in. Die hoofbydraes van hierdie studie sluit twee nuwe tegnieke, wat op die bou van 'n robuuste skrywer-onafhanklike handtekeningmodel gerig is, in. Die eerste, 'n dinamiese tydsverbuiging digotomie-transformasie vir statiese handtekening-voorstelling, is in staat om vir redelike intra-klas variasie te kompenseer, deur die DRT-projeksies voor vergelyking nie-lineêr te belyn. Die tweede, 'n skrywer-spesieke verskil-normaliseringstrategie, is in staat om inter-klas skeibaarheid in die verskilruimte te verbeter deur slegs streng relevante statistieke tydens die normalisering van verskil-vektore te beskou. Die normaliseringstrategie is generies van aard in die sin dat dit ewe veel van toepassing op beide statiese en dinamiese handtekening-modelkonstruksie is. Die stelsels wat in hierdie studie ontwikkel is, is spesi ek op die opsporing van hoë-kwaliteit vervalsings gerig. Stelselvaardigheid-afskatting word met behulp van 'n omvattende eksperimentele protokol bewerkstellig. Verskeie groot handtekening-datastelle is oorweeg. In beide die statiese en dinamiese gevalle vaar die voorgestelde SVM-gebaseerde stelsel beter as die voorgestelde KDA-gebaseerde stelsel. Ons toon ook aan dat die stelsels wat in hierdie studie ontwikkel is, die meeste bestaande stelsels wat op dieselfde datastelle ge evalueer is, oortref. Dit is selfs meer belangrik om daarop te let dat, wanneer hierdie stelsels met bestaande tegnieke in die literatuur vergelyk word, ons aantoon dat die gebruik van die nuwe tegnieke, soos in hierdie studie voorgestel, konsekwent tot 'n statisties beduidende verbetering in stelselvaardigheid lei. / ENGLISH ABSTRACT : In this dissertation we present a novel strategy for automatic handwritten signature verification. The proposed framework employs a writer-independent approach to signature modelling and is therefore capable of authenticating questioned signatures claimed to belong to any writer, provided that at least one authentic sample of said writer's signature is available for comparison. We investigate both the traditional off-line scenario (where an existing pen-on-paper signature is extracted from a digitised document) as well as the increasingly popular on-line scenario (where the signature data are automatically recorded during the signing event by means of specialised electronic hardware). The utilised off-line feature extraction technique involves the calculation of several discrete Radon transform (DRT) based projections, whilst on-line signatures are represented in feature space by several spatial and temporal function features. In order to facilitate writer-independent signature analysis, these feature sets are subsequently converted into a dissimilarity-based representation by means of a suitable dichotomy transformation. The classification techniques utilised for signature modelling and verification include quadratic discriminant analysis (QDA) and support vector machines (SVMs). The major contributions of this study include two novel techniques aimed towards the construction of a robust writer-independent signature model. The first, a dynamic time warping (DTW) based dichotomy transformation for off-line signature representation, is able to compensate for reasonable intra-class variability by non-linearly aligning DRT-based projections prior to matching. The second, a writer-specific dissimilarity normalisation strategy, improves inter-class separability in dissimilarity space by considering only strictly relevant dissimilarity statistics when normalising the dissimilarity vectors belonging to a specific individual. This normalisation strategy is generic in the sense that it is equally applicable to both off-line and on-line signature model construction. The systems developed in this study are specifically aimed towards skilled forgery detection. System proficiency estimation is conducted using a rigorous experimental protocol. Several large signature corpora are considered. In both the off-line and on-line scenarios, the proposed SVM-based system outperforms the proposed QDA-based system. We also show that the systems proposed in this study outperform most existing systems that were evaluated on the same data sets. More importantly, when compared to state-of-the-art techniques currently employed in the literature, we show that the incorporation of the novel techniques proposed in this study consistently results in a statistically significant improvement in system proficiency.

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

Signatures have been used to authenticate documents and transactions for over 1500 years and are still being used today. In this project a method for verifying signatures written on a tablet has been developed and tested in order to test whether pressure information is vital for a well performing on-line signature verification systems. First a background study was conducted to learn about the state-of-the-art methods and what features several research systems used, then the method was developed. The method is a Dynamic Time Warp with 8 local features, 2 of them were pressure values or derived from pressure, and 1 global feature. The developed method was tested on SUSig visual corpus containing signatures from 94 persons. The Equal Error Rate (EER) when not using pressure was 5.39 % for random forgeries and 3.24 % for skilled forgeries. EER when using pressure was 5.19 % for random forgeries and 2.80 % for skilled forgeries. The background study concluded that pen inclination is not required for a well performing system. Considering the result of this project and the result of others, it seems that pressure information is not vital, but provide some valuable information that can be used to classify signatures more accurately. / Signaturer har blivit använda för att autentisera dokument och transaktioner i över 1500 år och används än idag. En metod för att testa signaturer skrivna på en digital platta har utvecklats för att testa huruvida tryckkänslighet och vinkeln på pennan är kritiskt för ett välpresterande on-line signature verification system. Först så genomfördes en bakgrundsstudie för att se hur andra moderna metoder gör och vad för features de använder för att sen utveckla metoden. Den använda metoden är en Dynamic Time Warp med 8 lokala features varav 2 är tyckkänslighet eller utvunna från tryckkänslighet samt en global feature. Metoden testades sedan på SUSig visual corpus som har signaturer från 94 personer. Equal Error Rate (EER) för de feature kombinationerna som inte använde tryckkänslighet blev 5.39 % för slumpmässiga signaturer och 3.24 % för förfalskningar. EER för kombinationer av features som innehåller tryckkänslighet blev 5.19 % för slumpmässiga signaturer och 2.80 % för förfalskningar. Givet resultatet av det här projektet samt andra projekt utforskade i bakgrundsstudien så verkar tryckkänslighet inte vara kritiskt men ger en del värdeful information för klassificera signaturer mer träffsäkert. Bakgrundsstudien gav att vinkeln på pennan inte var kritisk för att välpresterande system.

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.

An enormous amount of the historical record is currently trapped in non-indexed handwritten format. Even after being scanned into images, only a minute fraction of the existing records can be manually transcribed / indexed with reasonable amounts of time and cost. Although progress continues to be made with automatic handwriting recognition (HR), it is not yet good enough to replace manual transcription or indexing. Much of the recent HR work has focused on incremental improvements to methods based on Hidden Markov Models (HMMs) and other similar probabilistic approaches. In this dissertation we present a fundamentally new approach to HR based on 2-D geometric warping of word images. The results of our experimentation indicate that our approach is significantly more accurate than an existing whole-word approach used for word-spotting, and may also be better than HMM-based HR approaches. Since it is a completely new method, we also believe there is potential for improvement and future work that builds on this approach. In addition, we demonstrate that the approach can be used effectively in the related application domain of signature verification and forgery detection.

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Thesis (MSc)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: An off-line signature verification system attempts to authenticate the identity of an individual by examining his/her handwritten signature, after it has been successfully extracted from, for example, a cheque, a debit or credit card transaction slip, or any other legal document. The questioned signature is typically compared to a model trained from known positive samples, after which the system attempts to label said signature as genuine or fraudulent. Classifier fusion is the process of combining individual classifiers, in order to construct a single classifier that is more accurate, albeit computationally more complex, than its constituent parts. A combined classifier therefore consists of an ensemble of base classifiers that are combined using a specific fusion strategy. In this dissertation a novel off-line signature verification system, using a multi-hypothesis approach and classifier fusion, is proposed. Each base classifier is constructed from a hidden Markov model (HMM) that is trained from features extracted from local regions of the signature (local features), as well as from the signature as a whole (global features). To achieve this, each signature is zoned into a number of overlapping circular retinas, from which said features are extracted by implementing the discrete Radon transform. A global retina, that encompasses the entire signature, is also considered. Since the proposed system attempts to detect high-quality (skilled) forgeries, it is unreasonable to assume that samples of these forgeries will be available for each new writer (client) enrolled into the system. The system is therefore constrained in the sense that only positive training samples, obtained from each writer during enrolment, are available. It is however reasonable to assume that both positive and negative samples are available for a representative subset of so-called guinea-pig writers (for example, bank employees). These signatures constitute a convenient optimisation set that is used to select the most proficient ensemble. A signature, that is claimed to belong to a legitimate client (member of the general public), is therefore rejected or accepted based on the majority vote decision of the base classifiers within the most proficient ensemble. When evaluated on a data set containing high-quality imitations, the inclusion of local features, together with classifier combination, significantly increases system performance. An equal error rate of 8.6% is achieved, which compares favorably to an achieved equal error rate of 12.9% (an improvement of 33.3%) when only global features are considered. Since there is no standard international off-line signature verification data set available, most systems proposed in the literature are evaluated on data sets that differ from the one employed in this dissertation. A direct comparison of results is therefore not possible. However, since the proposed system utilises significantly different features and/or modelling techniques than those employed in the above-mentioned systems, it is very likely that a superior combined system can be obtained by combining the proposed system with any of the aforementioned systems. Furthermore, when evaluated on the same data set, the proposed system is shown to be significantly superior to three other systems recently proposed in the literature. / AFRIKAANSE OPSOMMING: Die doel van ’n statiese handtekening-verifikasiestelsel is om die identiteit van ’n individu te bekragtig deur sy/haar handgeskrewe handtekening te analiseer, nadat dit suksesvol vanaf byvoorbeeld ’n tjek,’n debiet- of kredietkaattransaksiestrokie, of enige ander wettige dokument onttrek is. Die bevraagtekende handtekening word tipies vergelyk met ’n model wat afgerig is met bekende positiewe voorbeelde, waarna die stelsel poog om die handtekening as eg of vervals te klassifiseer. Klassifiseerder-fusie is die proses waardeer individuele klassifiseerders gekombineer word, ten einde ’n enkele klassifiseerder te konstrueer, wat meer akkuraat, maar meer berekeningsintensief as sy samestellende dele is. ’n Gekombineerde klassifiseerder bestaan derhalwe uit ’n ensemble van basis-klassifiseerders, wat gekombineer word met behulp van ’n spesifieke fusie-strategie. In hierdie projek word ’n nuwe statiese handtekening-verifikasiestelsel, wat van ’n multi-hipotese benadering en klassifiseerder-fusie gebruik maak, voorgestel. Elke basis-klassifiseerder word vanuit ’n verskuilde Markov-model (HMM) gekonstrueer, wat afgerig word met kenmerke wat vanuit lokale gebiede in die handtekening (lokale kenmerke), sowel as vanuit die handtekening in geheel (globale kenmerke), onttrek is. Ten einde dit te bewerkstellig, word elke handtekening in ’n aantal oorvleulende sirkulêre retinas gesoneer, waaruit kenmerke onttrek word deur die diskrete Radon-transform te implementeer. ’n Globale retina, wat die hele handtekening in beslag neem, word ook beskou. Aangesien die voorgestelde stelsel poog om hoë-kwaliteit vervalsings op te spoor, is dit onredelik om te verwag dat voorbeelde van hierdie handtekeninge beskikbaar sal wees vir elke nuwe skrywer (kliënt) wat vir die stelsel registreer. Die stelsel is derhalwe beperk in die sin dat slegs positiewe afrigvoorbeelde, wat bekom is van elke skrywer tydens registrasie, beskikbaar is. Dit is egter redelik om aan te neem dat beide positiewe en negatiewe voorbeelde beskikbaar sal wees vir ’n verteenwoordigende subversameling van sogenaamde proefkonynskrywers, byvoorbeeld bankpersoneel. Hierdie handtekeninge verteenwoordig ’n gereieflike optimeringstel, wat gebruik kan word om die mees bekwame ensemble te selekteer. ’n Handtekening, wat na bewering aan ’n wettige kliënt (lid van die algemene publiek) behoort, word dus verwerp of aanvaar op grond van die meerderheidstem-besluit van die basis-klassifiseerders in die mees bekwame ensemble. Wanneer die voorgestelde stelsel op ’n datastel, wat hoë-kwaliteit vervalsings bevat, ge-evalueer word, verhoog die insluiting van lokale kenmerke en klassifiseerder-fusie die prestasie van die stelsel beduidend. ’n Gelyke foutkoers van 8.6% word behaal, wat gunstig vergelyk met ’n gelyke foutkoers van 12.9% (’n verbetering van 33.3%) wanneer slegs globale kenmerke gebruik word. Aangesien daar geen standard internasionale statiese handtekening-verifikasiestelsel bestaan nie, word die meeste stelsels, wat in die literatuur voorgestel word, op ander datastelle ge-evalueer as die datastel wat in dié projek gebruik word. ’n Direkte vergelyking van resultate is dus nie moontlik nie. Desnieteenstaande, aangesien die voorgestelde stelsel beduidend ander kenmerke en/of modeleringstegnieke as dié wat in bogenoemde stelsels ingespan word gebruik, is dit hoogs waarskynlik dat ’n superieure gekombineerde stelsel verkry kan word deur die voorgestelde stelsel met enige van bogenoemde stelsels te kombineer. Voorts word aangetoon dat, wanneer op dieselfde datastel geevalueerword, die voorgestelde stelstel beduidend beter vaar as drie ander stelsels wat onlangs in die literatuur voorgestel is.