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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/98042 |
| Date | 12 1900 |
| Creators | Swanepoel, Jacques Philip |
| Contributors | Coetzer, Johannes, Stellenbosch University. Faculty of Science. Department Mathematical Sciences (Applied Mathematics) |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | xvii, 135 pages, illustrations (some colour) |
| Rights | Stellenbosch University |
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