Fusion of directional transitional features for off-line signature verification

dc.contributor.author	Τσέλιος, Κωνσταντίνος	el
dc.contributor.author	Οικονόμου, Γεώργιος	el
dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.contributor.author	Νασιόπουλος, Αθανάσιος Α.	el
dc.date.accessioned	2015-01-09T17:58:59Z
dc.date.issued	2015-01-09
dc.identifier.uri	http://hdl.handle.net/11400/3645
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	Signature verification
dc.subject	Grid feature
dc.subject	Επαλήθευση υπογραφής
dc.subject	Χαρακτηριστικό πλέγμα
dc.title	Fusion of directional transitional features for off-line signature verification	en
heal.type	conferenceItem
heal.classification	Electrical engineering
heal.classification	Electronics
heal.classification	Ηλεκτρολογική μηχανική
heal.classification	Ηλεκτρονική
heal.classificationURI	http://skos.um.es/unescothes/C01311
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Ηλεκτρολογική μηχανική
heal.classificationURI	N/A-Ηλεκτρονική
heal.identifier.secondary	DOI: 10.1109/IJCB.2011.6117515
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	Tselios, K., Economou, G., Zois, E. and Nassiopoulos, A. (2011). Fusion of directional transitional features for off-line signature verification. In the Biometrics (IJCB), International Joint Conference on Biometrics Compendium. Washington, 11th-13th October 2011. pp. 1-6. IEEE.	en
heal.abstract	n this work, a feature extraction method for off-line signature recognition and verification is proposed, described and validated. This approach is based on the exploitation of the relative pixel distribution over predetermined two and three-step paths along the signature trace. The proposed procedure can be regarded as a model for estimating the transitional probabilities of the signature stroke, arcs and angles. Partitioning the signature image with respect to its center of gravity is applied to the two-step part of the feature extraction algorithm, while an enhanced three-step algorithm utilizes the entire signature image. Fusion at feature level generates a multidimensional vector which encodes the spatial details of each writer. The classifier model is composed of the combination of a first stage similarity score along with a continuous SVM output. Results based on the estimation of the EER on domestic signature datasets and well known international corpuses demonstrate the high efficiency of the proposed methodology.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	Biometrics (IJCB), International Joint Conference on Biometrics Compendium, IEEE	en
heal.conferenceItemType	full paper