Grid-based feature distributions for off-line signature
verification

dc.contributor.author	Τσέλιος, Κωνσταντίνος	el
dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.contributor.author	Σιώρης, Ηλίας	el
dc.contributor.author	Νασιόπουλος, Αθανάσιος Α.	el
dc.contributor.author	Οικονόμου, Γεώργιος	el
dc.date.accessioned	2015-01-09T13:59:38Z
dc.date.issued	2015-01-09
dc.identifier.uri	http://hdl.handle.net/11400/3624
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	Image classification
dc.subject	Handwriting recognition
dc.subject	Ταξινόμηση εικόνων
dc.subject	Αναγνώριση χειρογράφου
dc.title	Grid-based feature distributions for off-line signature verification	en
heal.type	journalArticle
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: 10.1049/iet-bmt.2011.0011
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	2012
heal.bibliographicCitation	Tselios, K., Zois, E., Siores, E., Nassiopoulos, A. and Economou G. (March 2012). Grid-based feature distributions for off-line signature verification. IET Biometrics. 1(1). pp. 72-81. The Institution of Engineering and Technology.	en
heal.abstract	In this work, a feature extraction method is presented for handwritten signature verification. The proposed algorithm models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two- and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Various partitions of the signature image into a group of sub-images were applied in order to define the overall dimensionality of the feature. In order to evaluate the classification efficiency of the introduced method, a number of verification strategies are implemented by making use of two internationally accepted and one domestic datasets. In all schemes, similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Additionally, zoning the extracted feature vector into combinations of tetrads and heptads, which in turn are fed into the afore-mentioned classification schemes, is exploited. Results, derived from random or simple imitations as well as simulated (skilled) forgery indicate that the proposed method achieves noticeably low equal error rates and it is expected to provide a powerful discriminative representation of the handwritten signature.	en
heal.publisher	The Institution of Engineering and Technology	en
heal.journalName	IET Biometrics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true