Please use this identifier to cite or link to this item:
Title: A behavioral handwriting model for static and dynamic signature synthesis
Authors: Ferrer, Miguel A. 
Diaz, Moises 
Carmona-Duarte, Cristina 
Morales, Aythami
Keywords: 33 Ciencias tecnológicas
metadata.dc.subject.other: Biometric recognition
On-line and off-line synthetic generation
Signature verification
Motor equivalence theory
Kinematic theory of human movement
Issue Date: 2017
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence 
Abstract: The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter-and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2016.2582167
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence[ISSN 0162-8828],v. 39 (7494603), p. 1041-1053
Appears in Collections:Artículos

Show full item record


checked on Jul 13, 2019


checked on Jul 13, 2019

Page view(s)

checked on Jul 14, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.