Off-line signature verification by local granulometric sizedistributions
Sabourin, R.
Genest, G.
Preteux, F.J.
Lab. d'Imagerie de Vision et d'Intelligence Artificelle, Ecole de Technol. Superieure, Montreal, Que.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 1997
Volume: 19,
Issue: 9
On page(s): 976-988
ISSN: 0162-8828
References Cited: 37
CODEN: ITPIDJ
INSPEC Accession Number: 5719884
Digital Object Identifier: 10.1109/34.615447
Current Version Published: 2002-08-06
Abstract
A fundamental problem in the field of off-line signature
verification is the lack of a signature representation based on shape
descriptors and pertinent features. The main difficulty lies in the
local variability of the writing trace of the signature which is closely
related to the identity of human beings. In this paper, we propose a new
formalism for signature representation based on visual perception. A
signature image consists of 512×128 pixels and is centered on a
grid of rectangular retinas which are excited by local portions of the
signature. Granulometric size distributions are used for the definition
of local shape descriptors in an attempt to characterize the amount of
signal activity exciting each retina on the focus of the attention grid.
Experimental evaluation of this scheme is made using a signature
database of 800 genuine signatures from 20 individuals. Two types of
classifiers, a nearest neighbor and a threshold classifier, show a total
error rate below 0.02 percent and 1.0 percent, respectively, in the
context of random forgeries
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