Reliable online human signature verification systems
Lee, L.L.
Berger, T.
Aviczer, E.
Fac. of Electr. Eng., DECOM-FEE-UNICAMP, Campinos ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1996
Volume: 18,
Issue: 6
On page(s): 643-647
ISSN: 0162-8828
References Cited: 19
CODEN: ITPIDJ
INSPEC Accession Number: 5312824
Digital Object Identifier: 10.1109/34.506415
Current Version Published: 2002-08-06
Abstract
Online dynamic signature verification systems were designed and
tested. A database of more than 10,000 signatures in (x(t), y(t))-form
was acquired using a graphics tablet. We extracted a 42-parameter
feature set at first, and advanced to a set of 49 normalized features
that tolerate inconsistencies in genuine signatures while retaining the
power to discriminate against forgeries. We studied algorithms for
selecting and perhaps orthogonalizing features in accordance with the
availability of training data and the level of system complexity. For
decision making we studied several classifiers types. A modified version
of our majority classifier yielded 2.5% equal error rate and, more
importantly, an asymptotic performance of 7% false acceptance rate at
zero false rejection rate, was robust to the speed of genuine
signatures, and used only 15 parameter features
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
