Off-line signature verification using two step transitional features

dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.contributor.author	Νασιόπουλος, Αθανάσιος Α.	el
dc.contributor.author	Τσέλιος, Κωνσταντίνος	el
dc.contributor.author	Σιώρης, Ηλίας	el
dc.contributor.author	Οικονόμου, Γεώργιος	el
dc.date.accessioned	2015-01-12T16:21:33Z
dc.date.issued	2015-01-12
dc.identifier.uri	http://hdl.handle.net/11400/3850
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Off-line signature recognition
dc.subject	Computer systems--Verification
dc.subject	Αναγνώριση υπογραφής εκτός σύνδεσης
dc.subject	Επαλήθευση
dc.title	Off-line signature verification using two step transitional features	en
heal.type	conferenceItem
heal.generalDescription	Proceedings	en
heal.classification	Technology
heal.classification	Electronics
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρονική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρονική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008002946
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Zois, E., Nassiopoulos, A., Tselios, K., Sioes, E. and Economou, G. (2011). Off-line signature verification using two step transitional features. In the IAPR Conference on Machine Vision Applications. Nara, 13th-15th June 2011.	en
heal.abstract	In this work, a new approach for off-line signature recognition and verification is presented and described. A subset of the line, concave and convex family of curvature features is used to represent the signatures. Two major constraints are applied to the feature extraction algorithm in order to model the two step transitional probabilities of the signature pixels. Segmentation of the signature trace is enabled using a window which is centred upon the centre of mass of the thinned image. Partitioning of the image leads to a multidimensional feature vector which provides useful spatial details of the acquired handwritten image. The classification protocol followed in this work relies on a hard margin support vector machine. Our method was applied to two databases, the first taken from the literature while the second created by the authors. In order to provide comparable results for the first stage signature verification system, we have applied an already published feature extraction method while keeping the same classification protocol. Primary evaluation schemes on both corpuses provide very encouraging verification results for the Average Error.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	IAPR Conference on Machine Vision Applications	en
heal.conferenceItemType	full paper