Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/3850
Title: Off-line signature verification using two step transitional features
Authors: Ζώης, Ηλίας Ν.
Νασιόπουλος, Αθανάσιος Α.
Τσέλιος, Κωνσταντίνος
Σιώρης, Ηλίας
Οικονόμου, Γεώργιος
Item type: Conference publication
Conference Item Type: Full Paper
Keywords: Off-line signature recognition;Computer systems--Verification;Αναγνώριση υπογραφής εκτός σύνδεσης;Επαλήθευση
Subjects: Technology
Electronics
Τεχνολογία
Ηλεκτρονική
Issue Date: 12-Jan-2015
2011
Publisher: [χ.ό.]
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.
Description: Proceedings
Language: English
Citation: 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.
Conference: IAPR Conference on Machine Vision Applications
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/3850
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