An Improved Dominant Point Feature for Online Signature Verification.

hrsrBsl1fifid,ffi*] sf $*curity

n-Chief

of the

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Jemal. H. AbawaiY, Deakin University, Australia

piofessor ln Depa*ment of Computer Sclence at Purdue, USA

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An Improved

Dominant Point

Feature

for

Ontine

Signature

Verification

Danna Putral,

Yogi

Pratamaz, Oka Sudana3 and,qdi Purnawana t'2'i'aDepartntent

of

Information

Techn?logy, (ldayana t)niversity, Indonesia

' ikgdarmaputra@gmail. com, l yo gipratama. ib@gmail. com,

1

agungo kas _@unud. ac. id, a dos ena di@yaho o. c om

Abstract

Among the biometrie characteristic, signature _forgery is the easiest way to do. Possibility

of

signature

forgery

similarity might be reaehed perfectly. This paper intoduced

a

new technique to improve dominant _{point feature} system based on its location

for

online signature verification. Dynamic Time lYarping

is

used

to

_{match two signature features vector.} The performance af system is tested by using 50 participants. Based on simulation result, system d,ccuracy without presence of the simple and trained impostors is 99,65% with rejectian error

is A% and acceptanee error is 0.i5%. llthile the current systems arefaced with the simple and

trained impostors, system accuracy became 9/,.04%

with

rejection

error

is

1.6% and an average af acceptance

elror is

7.i6% with details as

follaws;

aeceptance error

is

0.08%,

acceptance error of simple impostors is 4,4o/o, and acceptance error of trained impostors is

17.6%.The improved _feature within

fusion

is produce better accuracy significantly than

dominant pointfeature. Accuracy of the improvedfeature withinfusion

is

91.04%o, whereas

system dccuracy with just use the dominant pointfeature is 74.96%.

Keywords: Verification, Dominant Point, Biometric, Signature, Location

of

Dominant Points

l.Introduction

Research and development

of

the biometric verilication

of

hurnan beings especially the signatures has been widely applied. Several kinds of methods have been used to minimize the

level

of

signatrare forgery because signature is the easiest to forge when

it

compares to the other biometric characteristics

[].

Possibility

of

signatures similarity might be reached

perfectly. Few people realiee that the possibility of the direction of motion of the signature is

different for each person.

It

becomes the uniqueness of the signature itself, then for reasons

such as to minimize the possibility that the signature to be _{forged [2].}

Several methods

have been

applied

to

the

biometrics(especially signatures) as identification or distinguishing between people with each other, they ate _{domirant point [3],} stroke matching

[4],

based

on writing

speed

[5],

angle detection

[6, 7],

suppofi vector

machine

[8],

mouse based signatures

[9],

time

sequence[0],

localized

arc

pattern

[ll],dynamic

RBF

networks and

time

series motifs

[12],4

features (pen position, time, velocity, and pressure parameters)

[3],

_{local dominant orientation [14],} etc.

Several studies have used dominant point as a research object

or

as ah object feature

extraction such as planar curves _{[15], digital curve [16], handwritten} of some _{script [17,} 19],

and also signature detection. Recognition rate of the previous study that used dominant point as method

for

signature feature extraction

in

signature recognition

is

about 96% with 2A

rssN: 1738-9976 IJSIA

simple and trained impostors, the system cafl increase the accuracy more than 17 Yo, while

*iifio"t

simple and trained impostois can increase the accuracy more than 10 %. The online

*igntne

verification system

in

this paper is very feasible to-be developed and applied for

uritfr.nti.otioo

applicalions.

For

future work,

we

would

develop

mobile

signature authentication sYstem

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