On-Line Signature Verification Using Tablet PC Fernando Alonso-Fernandez Julian Fierrez-Aguilar Francisco del-Valle Javier Ortega-Garcia Biometrics Research Lab.-ATVS Escuela Politecnica Superior -Universidad Autonoma de Madrid

2025-05-02 0 0 3.61MB 6 页 10玖币
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On-Line Signature Verification Using Tablet PC
Fernando Alonso-Fernandez, Julian Fierrez-Aguilar, Francisco del-Valle, Javier Ortega-Garcia
Biometrics Research Lab.- ATVS, Escuela Politecnica Superior -Universidad Autonoma de Madrid
Avda. Francisco Tomas y Valiente,
11
-Campus de Cantoblanco -28049 Madrid, Spain
{ fernando.alonso, julian.fierrez, javier.ortega }@uam.es
Abstract
On-line signature verification
for
Tablet
PC
devices is
studied. The on-line signature verification algorithm pre-
sented by the authors
at
the First International Signature
Verification Competition (SVC 2004) is adapted to work in
Tablet
PC
environments.
An
example prototype
of
securing
access
and
securing document application using this Tablet
PC
system
is
also reported.
Two
different commercial Tablet
PCs are evaluated, including information
of
interest
for
sig-
nature verification systems such as sampling
and
pressure
statistics. Authentication performance experiments are re-
ported considering both random and skilled forgeries by us-
ing a new database with over 3000 signatures.
1.
Introduction
Automatic signature verification has been an intense re-
search field [
1,
2]
because
of
the social and legal acceptance
and the widespread use
of
the written signature
as
a personal
authentication method [3]. Nowadays, there is an increas-
ing use
of
portable personal devices capable
of
capturing
signature (e.g, Tablet PCs, PDAs, mobile telephones, etc)
which is producing a growing demand
of
person authenti-
cation applications based on signature signals.
In this work, we adapt the on-line signature verification
system from ATVS to work
in
Tablet PC environments. The
system was evaluated
in
the First International Signature
Verification Competition [ 4], where was ranked first and
second for random and skilled forgeries, respectively.
Additionally, we describe a database captured using two
different Tablet PCs. The scope
of
utility
of
this database
includes the performance assessment in the design
of
auto-
matic signature-based recognition systems using Tablet PC
in
several forensic, civil and commercial applications. This
new database is used to evaluate the ATVS signature verifi-
cation system for Tablet PC.
The rest
of
the paper is organized
as
follows. The
ATVS on-line signature verification algorithm is described
in Sect. 2. The application scenario
of
the prototype devel-
oped is described in Sect.
3.
The new database is described
in
Sect.
4.
Experimental procedure used to evaluate the pro-
totype and results are described in Sect.
5.
2. On-Line Signature Verification Based on
HMM
The signature verification system for Tablet PC is based
on the recognition algorithm from ATVS presented at the
First International Signature Verification Competition (SVC
2004) [ 4]. This section briefly describes the basics
of
the
recognition algorithm [5]. For further information, we refer
the reader to [6],
[7]
and the references therein. In Fig.
1,
the overall system model is depicted.
2.1. Feature Extraction
Coordinate trajectories (x[n], y[n]), n =
1,
...
, N8, and
pressure signal
p[n],
n =
1,
...
, N8, where N8 is the num-
ber
of
samples
of
the signature, are considered. Signature
trajectories are first preprocessed by subtracting the center
of
mass followed
by
a rotation alignment based on the aver-
age path tangent angle.
An extended set
of
discrete-time functions are then de-
rived from the preprocessed trajectories. The functions de-
rived consist
of
a sample by sample estimation
of
various
dynamic properties. As a result, the signature is param-
eterized
as
the following set
of
7 discrete-time functions
{ x[n],
y[n],
p[n],
0[n],
v[n],
p[n],
a[n]}, n =
1,
...
,
Ns,
and
first order derivatives
of
all
of
them, resulting
14
discrete
functions (
0,
v, p and a stand respectively for path tangent
angle, path velocity magnitude, log curvature radius and to-
tal acceleration magnitude). A whitening linear transforma-
tion is finally applied
to
each discrete-time function so as to
obtain zero mean and unit standard deviation function val-
ues.
2.2. Similarity Computation
Given the enrolment set
of
signatures
of
a client
T,
pa-
rameterized
as
described in Sect. 2.1, a left-to-right Hid-
den Markov Model ,\
7 is estimated by using the Baum-
Welch iterative algorithm [8], [9]. No transition skips be-
tween states are allowed and multivariate Gaussian Mixture
density observations are used (2 states and 32 mixtures per
state).
On the other hand, given a test signature S (with a du-
ration
of
N8 samples) and a claimed identity T modeled
as
Proceedings
of
the 4th International Symposium on Image and Signal Processing and Analysis (2005) 245
Input
Signature
ON-LINE SIGNATURE
Pre-
Processing Score
Normalization
~LA/MEO
USEF0
DECISION
THRESHOLD
--
Figure
1.
System model for signature person authentication.
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Secure access
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a Web-based user account
Figure 2. Screen captures of the developed prototypes.
>-
T,
the following similarity matching score, t, is computed
by using the Viterbi algorithm [8]:
1
t = -
logp
(Sl>-
7)
Ns
3. Application Scenario
Internet banking, networking, e-Government and other
new technologies have been increasingly used in the last
years. Ensuring security is an important problem in these
environments. Biometric-based solutions to this problem
are currently a major research topic [3].
In
this framework,
we
have designed a prototype for i)
secure access
to
a Web-based user account and
ii)
secure
encryption
of
electronic documents. Users are enrolled in
this prototype by providing 5 signatures in 2 different ses-
sions
(3
and 2 signatures
in
the first and second session re-
spectively).
246 Proc. ISPA05
In Fig. 2, screen captures
of
the developed prototype are
depicted.
4. Tablet PC Environment Data
4.1. Acquisition
The following Tablet PCs have been used: i) Hewlett-
Packard TC 1100 with Intel Pentium Mobile
1.1
Ghz pro-
cessor and 512 Mb RAM, and
ii)
Toshiba Portege M200
with Intel Centrino
1.6
Ghz processor and 256 Mb RAM.
Both
of
them provide the following discrete-time dynamic
sequences: position in x- and y-axis and pressure p. Mi-
crosoft Windows XP Tablet PC Edition 2005 has been used.
In Table
1,
an example
of
the discrete time sequences
provided is shown. Additionally, in Fig.
3,
the instanta-
neous sampling period
of
two entire signatures is depicted.
In Fig. 4(a), the instantaneous sampling period distribution
of
5 signatures
of
different individuals is shown. It can be
摘要：

On-LineSignatureVerificationUsingTabletPCFernandoAlonso-Fernandez,JulianFierrez-Aguilar,Franciscodel-Valle,JavierOrtega-GarciaBiometricsResearchLab.-ATVS,EscuelaPolitecnicaSuperior-UniversidadAutonomadeMadridAvda.FranciscoTomasyValiente,11-CampusdeCantoblanco-28049Madrid,Spain{fernando.alonso,julian...

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