On-line Finger-Knuckle-Print Identification Using Gaussian Mixture Models & Discrete Cosine Transform

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Nov 30, 2013 (3 years and 10 months ago)

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INTER
N
A
TIO
N
AL

CONFERENCE

ON

ELECT
R
ONICS

AND

OIL
-
ICEO2013

5
-
6

MARCH

2013,

O
U
ARGLA,

ALGERIA

1


P



On
-
line

Finge
r
-
Knuckle
-
Print

Identification

Using

Gaussian

Mixture

Models

&

Discrete

Cosine

T
ransform


Abdallah

Meraoumi
a
1
,Salim

Chitroub
2

and

Ahmed

Bouridan
e
3
,
4


1

Kasdi

Merbah

un
i
v
ersity

of

Oua
r
gla,

Electrical

engineering

Laboratory

Sciences

and

T
echn
ology

and

Mater

Sciences

F
acult
y
,

Oua
r
gla,

30000,

Algeria

2

Signal

and

Image

Processing

Laboratory,

Electronics

and

Computer

Science

F
acult
y
,

USTHB.

P
.O.

box

32,

El

Alia,

Bab

Ezzoua
r
,

16111,

Algiers,

Algeria

3

School

of

Computing,

Engineering

and

Informati
on

Sciences,

Northumbria

Un
i
v
ersit
y
,
P
andon

Building,

N
e
wcastle

upon

T
yne,

UK.

4

Department

of

Computer

Science,

King

Saudi

Un
i
v
ersit
y
,
P
.O.

Box

2454,

Riyadh,

11451,

Saudi

Arabia

Email:

Ame
r
aoumia@gmail.com,


S

c
hit
r
oub@hotmail.com,


Ahmed.Bouridane@northumbria.ac.uk,


ABouridan
e
.c@ksu.edu.sa





Abstrac
t

Biometric system

has

been

act
iv
e
ly

eme
r
ging

in
v
arious

industries

f
or

the

past

few

y
ears,

and

it

is

continuing to
r
oll

to

p
r
o
vide higher

security featu
r
es
f
or

access

cont
r
ol

system.
In

the

r
ecent

y
ears,

hand

based

biometrics

is

extens
iv
ely

used
f
or

personal

r
ecognition. In

this

pape
r
,

we

p
r
opose

an

efficient
online

personal

identification system

based

on

Finge
r
-
Knuckle
-

Print

(FKP)

using

the

Gaussian

Mixtu
r
e

Model

(
GMM

)

and
t
w
o
-
dimensional Block

Based

Disc
r
ete

Cosine

T
rans
f
orm

(
2D
-

BDCT

).

In

this

stud
y
,

a

segmented

FKP

is

firstly

d
i
vided

into
non
-
ov
erlapping

and

equal
-
sized

blocks,

and

then,

applies

the

2D
-
BDCT

ov
er

each

block. By

using

zigzag scan

order each
trans
f
orm block

is

r
eorde
r
ed to

p
r
oduce

the

featu
r
e

v
ecto
r
.
Subsequentl
y
,

we

use

the

GMM

f
or

modeling

the

featu
r
e

v
ector
of

each

FK
P
.

Finall
y
,

Log
-
li
k
elihood sco
r
es

a
r
e

used

f
or

FKP
matchin
g
.

Experimental
r
esults

sh
o
w

that

our

p
r
oposed

method
yields

the

best

per
f
ormance

f
or

identifying FKPs

and

it

is

able

to
p
r
o
vide

an

excellent

identification

rate

and

p
r
o
vide

mo
r
e

securit
y
.



Index

T
erms

Biometrics,

identification,

Finge
r
-
Knuckle
-
Print,

2D
-
BDCT,

GMM,

Data

fusion.



I
.

I
NT
R
ODUCTIO
N

ERSO
N
AL

identification

plays

a

critical

role

in

our

soci
-

et
y
.

T
raditional

kn
o
wledge

based

or

to
k
en
-
based

personal
identification

systems

are

time
-
consuming,

ine
f
ficient and

e
x
-

pens
i
v
e.

Biometrics

o
f
fers

a

natural

and

reliable

solution

to
the
problem

of

identity

determination

by

recognizing

ind
i
viduals
based

on

some

characteristics

that

are

inherent

to

the

person
[1].

Biometrics

is

a

study

of

metho
ds

for

uniquely

recogniz
-

ing

ind
i
viduals

based

on

one

or

more

intrinsic

p
h
ysical

or
beh
a
vioral

traits,

including

the

e
xtens
i
v
ely

studied

fingerprint,
iris,

speech,

hand

geometr
y
,

and

palmprint.

One

of

the

most
popular

biometric

systems

is

based

on

the

han
d

due

to

its

ease
of

use.

Recentl
y
, a

n
ov
el

hand
-
based biometric

feature,

finge
r
-

knuckle
-
print

(FKP),

has

attracted

an

increasing amount

of

at
-

tention

[2].

The

t
e
xture

pattern

produced

by

the

finger

knuckle
bending

is

highly

unique

and

ma
k
es

the

sur
f
ace

a

distinct
i
v
e
biometric

identifie
r
.

Li
k
e

a
n
y

other

biometric

identifiers,

FKPs
are

beli
e
v
ed to

h
a
v
e

the

critical

properties

of

un
i
v
ersalit
y
,

uniqueness,

and

permanence

for

personal

recognition

[3].

An

important

issue

in

FKP

identification is

to

e
xtr
act

FKP
features

that

can

discriminate an

ind
i
vidual

from

the

othe
r
.
Based

on

t
e
xture

analysis,

our

biometric

identification

system
used

the

2D
-
BDCT

for

features

e
xtracted

from

FKP

images.

In

this

method,

a

FKP

is

firstly d
i
vided

into

non
-
ov
erlapping
and

equalized blocks,

and

then,

applies

the

2D

discrete

cosine
transform
ov
er

each

block.

By

using

zigzag

scan

orde
r
,

each
transform

block

is

reordered

to produce

the

feature

v
ector
and

then

concatenated all

v
ectors

for

produce

an

obser
v
ation
v
ecto
r
.

Subsequ
entl
y
,

we use the GMM

for modeling

this
v
ector

(for

each

FKP).

Finall
y
,

log
-
li
k
elihood scores

are

used
for

matching.

In

this

w
ork,

a

series

of

e
xperiments were
carried

out

using a

FKP

database.

T
o

ev
aluate

the

e
f
ficien
c
y

of
this

technique,

th
e

e
xperiments were

designed

as

foll
o
w:

the
performances

under

di
f
ferent

finger types

were

compared

to
each

othe
r
,

in

order

to

determine

the

best

finger

type

at

which
the

FKP

identification

system

performs.

H
o
w
e
v
e
r
,

because

our
database

contains

FKPs

from

f
our

types

of

fingers, an

ideal
FKP

identification system

should

be

based

on

the

fusion

of
these

fingers

at

di
f
ferent

fusion

l
e
v
els.

The

rest

of

the

paper

is

o
r
g
anized as

foll
o
ws:

The

proposed
FKP

recognition scheme

is

presented

in

section

2.

Section

3
g
i
v
e
s

a

brief

description of

the

method

used

for

e
xtracting the
R
e
gion

Of

Interest

(
R
OI

).

The

feature

e
xtraction

and

modeling
process,

including

an

ov
ervi
e
w

of

the

t
w
o
-
dimensional block
based

discrete

cosine

transform and

the

g
aussian

mixture
model,

is

pres
ented

in

section

4.

A

sections 5

is

d
e
v
oted
to

describe

the

ev
aluation

and

normalization method.

The
obtained results,

prior

to

fusion

and

after

fusion,

are

ev
aluated
and

commented in

section

6.

Finall
y
,

conclusions and

future
w
ork

are

g
i
v
en

in

sect
ion

7.



II
.

S
YSTE
M

O
VE
R
VIE
W


Fig.

1

illustrates the

v
arious

modules

of

our

proposed uni
-

modal FKP

identification

system

(single finger).

The

pro
-

posed

system

consists

of

preprocessing,

feature

e
xtraction

and
modeling, matching

and

decision

stages.

T
o

enroll

into

the
system

database,

the

user

has to

pr
o
vide

a

set of

training
FKP

images.

T
ypicall
y
,

an

obser
v
ation

v
ector

is

e
xtracted

from
each

finger

which

describes

certain

characteristics

of

the

FKP
images

using

Discrete

Cosine

T
ransform

(DCT

)

technique

and
modeling using

g
aussian

mixture

model.

Finall
y
,

the

models
parameters are

stored

as

references

models.

F
or

identification,
the

same

obser
v
ation
v
ectors

are

e
xtracted

from

the

test

FKP
images and

the

log
-
li
k
elihood

is

computed

using

all

of

mo
dels

INTER
N
A
TIO
N
AL

CONFERENCE

ON

ELECT
R
ONICS

AND

OIL
-
ICEO2013

5
-
6

MARCH

2013,

O
U
ARGLA,

ALGERIA

2






½





GMM
-
Modeling

D
AT
A
B
ASE





Preprocessing

Feature

v
ector

Matching

Module

Decision



Fig.

1.

Block
-
diagram

of

the

FKP

identification

system

based

on

the

g
aussian

mixtu
re

model.



Image

original

Determine

the

X
-
axis

R
OI

Localisation

R
OI

Extraction








Fig.

2.

R
OI

e
xtraction

process.

(
a
)

Image

original;

(
b
)

X
-
axis

of

the

coordinate

system;

(
c
)

R
OI

coordinate

system

and

(
d
)

R
e
gion

of

interest

(
R
OI)



references. Our

database

contains

FKPs

from

four

types

of
fingers,

for

this

raison, each

FKP

modalities

are

used

as

inputs
of the

matcher

modules

(sub
-
system).

F
or

the

multi
-
modal
system,

each

sub
-
system compute

its

o
wn

matc
hing

score

and
these

ind
i
vidual

scores

are

finally combined

into

a

total

score
(using

fusion

at

the

matching

score

l
e
v
el),

which

is

used

by

the

and

a

1D
-
DCT

on

the

r
o
ws.

G
i
v
en

an

image

f

,

where

H
,

W
represent

their

size,

the

DCT

coe
f
ficients

of

the

spati
al

block
are

then

determined

by

the

foll
o
wing

formula:


M


1

M


1

F
ij

(
u,

v
)

=

C

(
v
)
C

(
u
)

X

X

f
ij

(
n,

m
)
ψ
(
n,

m,

u,

v
)

(1)

m
=0

n
=0

decision

module.

W
e

h
a
v
e

also

tried

the

fusion

at

the

decision

l
e
v
el

to

choose

the

best

one

for

FKPs

classification.


ψ
(
n,

m,

u,

v
)

=

cos

h

(2
n

+

1)


i

2
M


cos

h

(2
m

+

1)
v
π

i

2
M


(2)

u,

v

=

0
,

1
,

·

·

·

,

M



1,

i

=

1
,

·

·

·

,

η
1

,

j

=

1
,

·

·

·

,

η
2

with

III
.

R
EGIO
N

O
F

INTERES
T

EXTR
A
CTIO
N

After

the

image

is

captured,

it

is

pre
-
processed

to

obtain
only

the

area

information

of

the

FK
P
.

The

detailed

steps

η
1

=


H


W

M

,

η
2

=

M

and

F
ij

(
u,

v
)

are

the

DCT

coe
f
ficients

of

the

B
ij

block,

f
ij

(
n,

m
)

is

the

luminance

v
alue

of

the

pi
x
el

(
n,

m
)

of

the

B
ij

block,

and

for

pre
-
processing

process

are

as

foll
o
ws

[4]:

First,

apply

a

Gaussian

smoothing

ope
ration

to

the

original

image.

Second,


C

(
u
)

=




1





2


if

u

=

0


(3)

determine

the

X
-
axis

of

the

coordinate

system

fitted from

the
bottom

boundary

of

the

finger; the

bottom

boundary

of

the
finger

can

be

easily
e
xtracted

by

a

Can
n
y

edge

de
tecto
r
.

Third,
determine

the

Y
-
axis

of

the

coordinate

system

by

applying
a

Can
n
y

edge

detector

on

the

cropped

sub
-
image
e
xtracted
from

the

image

original

base

on

X
-
axis,

then

find the

co
n
ve
x
direction coding

scheme.

Finall
y
,

e
xtract

the

R
OI

coordinate
syst
em,

where

the

rectangle indicates the

area

of

the

R
OI

that
will

be

e
xtracted.

The

pre
-
processing

steps

are

sh
o
wn

in

Fig.

2
.


I
V
.

F
E
A
TUR
E

EXTR
A
CTIO
N

AN
D

M

ODELIN
G

A.

2D

Blo
c
k

based

disc
r
ete

cosine

t
r
ansform

Discrete

cosine

transform

is

a

p
o
werful

transform

to

e
xtract

1

if

u

=

0


After

transformation process,

if

M=8
,

there

will

be

64

DCT
coe
f
ficients contained

within

each

transformed

block,

where
the

coe
f
ficient

at

the

top
-
left

is

called

DC

h
F
ij

(0
,

0)
i

coe
f
fi
-

cient

and

the

rest

is

call
ed

A
C

coe
f
ficients.



B.

Observation

vector

The

block
-
based approach

partitions

the

input

image,

with
size

H

×

W

,

when

H

=

220

and

W

=

110,

into

small

non
-

ov
erlapped

blocks;

each

of

them

is

then

mapped

into

a

block
of

coe
f
ficients via

the

2D
-
DCT.

Most

popular

block

size

is
commonly

set

to

M

×

M

with

M=8
.

The

number

of

blocks
e
xtracted

from

each

FKP

image

equals

to:

proper

features

for

FKP

identification.

The

DCT

is

the

most
widely

used

transform

in

image

processing

algorithms,

such

η

=

b
η
1

c



b
η
2

c

=

b

220

8

c



b

110

8

c

=

27



13

=

351

blocks

(4)

as

image/video compression and

pattern

recognition.

Its

pop
-

ularity

is

due

mainly

to

the

f
act

that

it

achi
e
v
es a

good

data
compaction,

that

is,

it

concentrates

the

information

content

in
a

rela
t
i
v
ely

f
e
w

transform

coe
f
ficients

[5].

In

the

2D
-
BDCT

formulation,

the

input

image

is

first

d
i
vided

into

η
1

×
η
2

blocks,

and

the

2D
-
DCT

of

each

block

is

determined.

The

2D
-
DCT

can

be

obtained

by

performing

a

1D
-
DCT

on

the

columns

Then,

we

form

a

feature
v
ector

from

the

2D
-
DCT

coe
f
ficients
of

each

image

block

(see

Fig.

3
).

The

2D
-
DCT

concentrates
the

information content

in

a

relat
i
v
ely

f
e
w

transform

coe
f
fi
-

cients

top
-
left

zone

of

block,

for

this,

the

coe
f
ficients, where
the

information

is

concentrated,

ten
d

to

be

grouped

together

at
the

start

of

the

reordered

arra
y
, Thus, a

suitable

scan

order

is

a
zigzag

starting

from

the

DC

(top
-
left)

coe
f
ficient

[6].

Starting

INTER
N
A
TIO
N
AL

CONFERENCE

ON

ELECT
R
ONICS

AND

OIL
-
ICEO2013

5
-
6

MARCH

2013,

O
U
ARGLA,

ALGERIA

3


|

|

j
=1





Fig.

3.

Obser
v
ation

v
ector

e
xtraction.

(a)

blocks

e
xtraction,

(b)

block

feature

e
xtract
ion

and

(c)

Obser
v
ation

v
ecto
r
.



with

the

DC

coe
f
ficient,

each

coe
f
ficient

is

copied

into

a

one
-

dimensional arra
y
.

So,

each

block

can

be

represented

by

a
v
ector

of

coe
f
ficients:

O
ij


=

[
F
ij

(0
,

0)

F
ij

(0
,

1)

F
ij

(1
,

0)

·

·

·

·

·

·

F
ij

(
U
,

V

)]
T


(5)


U,

V

are

chosen

as

well

as

the

identification

rate

w
as

max
-

imum.

Thus,

U,

V



[0

·

·

7]

and

the

size

of

O
ij


is

τ


with
τ




[1

·

·

64].

Finall
y
,

the

results

o
ij


of

a

blocks

image

are
combined

in

the

single

template

as

foll
o
ws:

V
O
bs

=

[
O
11

O
12

O
13

O
14

·

·

·

O
η
1

η
2

]

(6)


Where

the

size

of

result
ing

obser
v
ation

v
ector

is

[
τ


η
]
.



C.

Gaussian

mixtu
r
e

model

Gaussian

mixture

model

is

pattern

recognition

technique
that

uses

an

approach of

the

statistical methods

[7].

The
obser
v
ation

v
ector

of

each

class

measurement

can

be

described
by

normal

distri
b
ution,

also

called

Gaussian

distri
b
ution.

Each
class

measurement

may

be

then

defined by

t
w
o

parameters:
mean

(
a
v
erage)

and

standard

d
e
viation

(
v
ariability). Suppose
that

the

obser
v
ation

v
ector

is

the

discrete

random

v
ariable
V
O
bs
.

F
or

the

general

case,

w
here

v
ector

is

multidimensional,
the

probability density

function

of

the

normal

distri
b
ution is

a
g
aussian

function:

P

(
V
O
bs

|
µ,

Σ
)

=

The

EM

is

the

ideal

candidate

for

solving

parameter

estimation
problems for

the

GMM.

Each

of

the

EM

iterations consists

of
t
w
o

steps

Estimation

(E)

and

Maximization (M).

The

M
-
step
maximizes

a

li
k
elihood

function

that

is

refined

in

each

iteration
by

the

E
-
step.


V
.

F
E
A
TUR
E

M

A
TCHIN
G

AN
D

NORMALIZ
A
TIO
N

After

e
xtracting

the

obser
v
ation

v
ectors

corresponding

to
the

test

images,

the

probability

of

the

obser
v
ation sequence
g
i
v
en

a

GMM

model

is

computed. The

model

with

the
highest

log
-
li
k
elihood i
s

selected

and

this

model

r
e
v
eals

the
identity

of

the

unkn
o
wn

finge
r
.

Thus,

during

the

identification
process,

the

characteristics of

the

test

image

are

e
xtraction

by
the

2D
-
BDCT

corresponding to

each

person.

Then

the

Log
-

li
k
elihood

score

of

the

obser
v
ati
on

v
ectors

g
i
v
en

each

model,
P

(
V
O
bs

|
θ
i

)

=

`
(
V
O
bs

,

θ
i

),

is

computed

[8].

Therefore,

the
score

v
ector

is

g
i
v
en

by:

L
(
V
O
bs

)

=

[
`
(
V
O
bs

,

θ
1

)

`
(
V
O
bs

,

θ
2

)
·

·

·

`
(
V
O
bs

,

θ
D

)]

(9)

Where

D

represents

the

size

of

model

database.

An

important

aspect

that

has

to

be

addressed in

identifi
-

cation

process

is

the

normalization of

the

scores

obtained.
Normalization typically

i
n
v
ol
v
es

mapping

the

scores

obtained
into

a

common domain.

Thus,

a

Min
-
Max
normalization
scheme

w
as

empl
o
yed

to

transform

the

Log
-
li
k
elihood scores
computed

into

similarity

scores

in

the

same

range.




1


e
xp

h

p
(2
π
)
d


Σ




1

(
V
O
bs

2



µ
)
T

Σ

1

(
V
O
bs

i



µ
)

(7)


L
N

=

L



min
(
L
)


(10)

max
(
L
)



min
(
L
)


where

µ

is

the

mean,

Σ


is

the

c
o
v
ariance

matrix

and

d

is

Where

L
N

denotes

the

normalized

Log
-
li
k
elihood

scores.

the

dimension

of

fea
ture

v
ecto
r
.

C
o
v
ariance matrix

is

the
natural

generalization to

higher

dimensions

of

the

concept

of
the

v
ariance of

a

random

v
ariable. If

we

suppose the

random
v
ariable

measurement is

not

characterized only

with

simple
g
aussian

distri
b
ution, we

can

the
n

define

it

with

multiple
g
aussian

components.

GMM

is

a

probability

distri
b
ution

that
is

a

co
n
ve
x

combination

of

other

g
aussian

distri
b
utions:


N

P

(
V
O
bs

)

=

X

π
j

P

(
V
O
bs

|
µ
j

,

Σ
j

)

(8)

j
=1

where

N

is

the

number

of

Gaussian mixtures and

π
j

is

the
weight

of

each

of

the

mixture.

After

GMM is

trained,

the
model

of

each

user

will

be

the

final

v
alues

of

π
j

,

µ
j


and

Σ
j

.

Thus,

the

compact

notation

θ
,

such

that

θ

=

{
π
j

,

µ
j

,

Σ
j

}
N

,
is

used

to

represent a

model.

T
o

estimate

the

density

para
-

meters

of

a

GMM

statistic

model,

cluster

estimation method
called

Expectation
-
maximization

algorithm

(EM)

is

adopted.

H
o
w
e
v
e
r
,

these

scores

are

com
pared,

and

the

highest

score

is

selected.

Therefore,

the

best

score

is

D
o

and

its

equal

to:

D
o

=

max(
L
N

)

(11)

i

Finall
y
,

this

score

is

used

for

decision

making.


VI
.

E
XPERI
M

EN
T
A
L

RESU
L
T
S

AN
D

DISCUSSIO
N

A.

Exper
imental

database

W
e

e
xperimented our

method

on

Hong

K
ong

polytechnic
un
i
v
ersity

(PolyU)

FKP

Database [9].

The

database has

a
total

of

7920

images

obtained from

165

persons. this

database
including 125

males

and

40

females.

Among

them,

143
subjects a
re

20

30

years

old

and

the

others

are

30

50

years
old.

these

images

are

collected

in

t
w
o

separate

sessions.

In
each

session, the

subject

w
as

as
k
ed

to

pr
o
vide 6

images

for
each

of

Left

Ind
e
x

Fingers (LIF),

Left

Middle Fingers (LMF),
Right

Ind
e
x

Fingers

(RIF
)

and

Right

Middle

Fingers (RMF).
Therefore,

48

images

were

collected

from

each

subject.

INTER
N
A
TIO
N
AL

CONFERENCE

ON

ELECT
R
ONICS

AND

OIL
-
ICEO2013

5
-
6

MARCH

2013,

O
U
ARGLA,

ALGERIA

4






D
A
T
A
B
ASE



T
o

F
AR

FRR

T
o

F
AR

FRR

T
o

F
AR

FRR

T
o

F
AR

FRR


0.9600

8.808

1.556

0.9600

7.970

1.630

0.6500

8.777

2.963

0.9400

7.820

1.482

165

Persons

0.9740

3.445

3.445

0.9717

3.874

3.874

0.9644

4.173

4.173

0.9586

3.318

3.318


0.9850

1.158

7.630

0.9800

1.866

6.593

0.9800

1.332

7.630

0.9800

0.731

7.259





(
a
)

(
b
)

(
c
)


F
ig.

4.

Uni
-
modal

open

set

identification

system

performance.

(a)

System

performance

under

di
f
ferent

2D
-
BDCT

coe
f
ficients

number

in

each

block

and
v
arious

GMM,

(b)

The

R
OC

cur
v
es

for

all

GMMs

and

(c)

The

R
OC

cur
v
es

for

all

finger

types.



T
ABLE

1

:

OP
EN

SET

IDENTIFIC
A
TION

TEST

RESU
L
T

IN

THE

CASE

OF

UNI
-
MO
D
AL

SYSTEM

LEFT

INDEX

FINGER

LEFT

MIDDLE

FINGER

RIGHT

INDEX

FINGER

RIGHT

MIDDLE

FINGER








B.

Evaluation

criteria

The

measure

of

utility

of

a
n
y

biometric

recognition system
for

a

particular

application

can

be

e
xplained

by

t
w
o

v
alues
[10].

The

v
alue

of

the

F
alse

Acceptance Rate

(
F
AR)

criterion,
which

is

the ratio

of

the number

of

instances

of

di
f
ferent
feature

pairs

of

the

t
raits

found

do

match

to

the

total

number
of

counterpart attempts,

and

the

v
alue

of

the

F
alse

Rejection
Rate

(FRR

)

criterion,

which

is

the

ratio

of

the

number

of
instances

of

same

feature

pairs

of

the

traits

found

do

not

match
to

the

total

number

of

counte
rpart attempts.

It

is

clear

that

the
system

can

be

adjusted to

v
ary

the

v
alues

of

these

t
w
o

criteria
for

a

particular

application. H
o
w
e
v
e
r
,

decreasing

one

i
n
v
ol
v
es
increasing

the

other

and

vice

v
ersa.

The

system

threshold

v
alue
is

obtained

using

Equal

Erro
r

Rate

(EER

)

criteria

when

F
AR

=

FRR.

This

is

based

on

the

rationale

that

both

rates

must

be
as

l
o
w

as

possible

for

the

biometric

system

to

w
ork

e
f
fect
i
v
el
y
.
Another

performance

measurement is

obtained

from

F
AR
and

FRR,

which

is

the

Genuine

Acc
eptance

Rate

(GAR

).
It

represents

the

identification

rate

of

the

system.

In

order
to

visually

describe

the

performance of

a

biometric

system,
Rece
i
v
er

Operating

Characteristics (
R
OC)

cur
v
es

are

usually
g
i
v
en. A

R
OC

cur
v
e

sh
o
ws h
o
w

the

F
AR

v
alues are

chan
ged
relat
i
v
ely

to

the

v
alues

of

the

GAR

and

vice
-
v
ersa [11].
Biometric

recognition systems

generate

matching

scores

that
represent

the

d
e
gree

of

similarity

(or

dissimilarity)

between
the

input

and

the

stored

template.


C.

2D
-
BDCT

coe
f
ficients

selection

in

ea
c
h

blo
c
k

The

2D
-
BDCT coe
f
ficients

reflect

the

compact

ene
r
gy

of
di
f
ferent

frequencies.

Most

of

the

higher

frequen
c
y

coe
f
ficients
are

small

and

th
e
y

become

n
e
gligible, as

result,

the

features
der
i
v
ed

from

the

2D
-
BDCT

computation is

limited

to

an
array

of

summed

spectral ene
r
gies

within

a

block

in

frequen
c
y
domain.

In

this

section,

we

present

the

identification

accura
c
y

of

our

system

as

a

function of

the

number

of

2D
-
BDCT
coe
f
ficients

(in

each

block)

used.

The

performance

ev
aluation
w
as

repeated f
or

v
arious

numbers of

2D
-
BDCT

coe
f
ficients
and

v
arious

numbers

of

GMM,

and

the

results

are

as

sh
o
wn
in

Fig.

4
.
(a)
.

The

reason

Fig.

4
.
(a)
w
as generated

w
as

to
sh
o
w

h
o
w

the

number of

2D
-
BDCT

coe
f
ficients

selection
in

each

block

and

the

number

of

GMMs

used

might

h
a
v
e
an

e
f
fect

on

the

performance of

our

system.

W
e

obser
v
e
that

the

identification accura
c
y

becomes

v
ery

high

at

certain
coe
f
ficients

and

slight

decrease

in

identification

accura
c
y

as

we
go

to

higher

numbers

of

coe
f
ficients.

F
or

e
xample,

if

1
-
GMM
with

28

coe
f
ficients

in

each

block,

is

used

for

the

identification,
we

h
a
v
e

a

GAR

equal

to

91.480 %.

In

the

case

of

using

2
-

GMM with

24

coe
f
ficients

in

each

block,

GAR

w
as

95.291

%.

3
-
GMM

with

22

coe
f
ficients in

each

block,

impr
ov
es

th
e
result

(GAR

=

96.467

%)

for

a

database size

equal

to

165
persons. In

Fig.

4
.
(b)
,

we

compare the

system

performance
under

di
f
ferent

GMMs.

The

results

sh
o
w

the

benefits

of

using

3
-
GMM.

Thus,

the

performance of

the

open

set

uni
-
modal
identification

system

i
s

significantly

impr
ov
ed

by

using

the

3
-
GMM

with

22

coe
f
ficients

in

each

block.



D.

Uni
-
modal

system

test

r
esults

T
o

ev
aluate

the

e
f
ficien
c
y of

the

uni
-
modal

biometric
method,

the

e
xperiments were

designed

as

foll
o
w:

three

sam
-

ples

(for

each

finger) of

e
ach

person

is

randomly

selected

for
enrollment,

and

the

rest

nine

finger images

are

used

as

test
samples

for

identification. Thus,

123255

comparisons

were
generated

for

performance
ev
aluation

(165

persons).

In

this
section

we

compare

the

performance

of

all

finger

types.

In
the

case

of

open

set

identification, Fig.

4
.
(c)

compares

the
performance

of

the

system

for

deferent

finger types.

It

can
safely

be

see

the

benefits

of

using

the

RMF

finger

than

the

LI
F
,
LMF

and

RIF

fingers

in

terms

of

EER.

It

can

be

achi
e
v
e

an

INTER
N
A
TIO
N
AL

CONFERENCE

ON

ELECT
R
ONICS

AND

OIL
-
ICEO2013

5
-
6

MARCH

2013,

O
U
ARGLA,

ALGERIA

5



SUM




WH
T




MIN




MAX




MUL


T
o


EER


T
o


EER


T
o


EER


T
o


EER


T
o


EER

0.9795


1.108


0.9795


1.115


0.9876


1.601


0.9723


1.562


0.9596


1.092

0.9735


1.519


0.9749


1.333


0.9851


1.874


0.9642


2.177


0.9483


1.489

0.9708


1.482


0.9701


1.510


0.9848


1.512


0.9627


1.770


0.9434


1.442

0.9664


1.577


0.9672


1.466


0.9833


1.447


0.9568


2.000


0.9354


1.556

0.9791


0.376


0.9792


0.375


0.9964


0.869


0.9607


1.239


0.9190


0.370




T
ABLE

2

:

OPEN

SET

IDENTIFIC
A
TION

TEST

RESU
L
T

IN

THE

CASE

OF

THE

FUSION

A
T

M
A
TCHING

SCORE

LEVEL


COMBI
N
A
TION


LIF
-
LMF
LIF
-
RIF
LMF
-
RMF
RIF
-
RMF

All

Fingers



T
ABLE

3

:

OPEN

SET

IDENTIFIC
A
TION

TEST

RESU
L
T

IN

THE

CASE

OF

THE

FUSION

A
T

DECISION

LEVEL


LIF
-
LMF
-
RIF


LIF
-
LMF
-
RMF


RIF
-
RMF
-
LIF


RIF
-
RMF
-
LMF


All

Fingers


F
AR

FRR

GAR


F
AR

FRR

GAR


F
AR

FRR

GAR


F
AR

FRR

GAR


F
AR

FRR

GAR

2.341

1.111

97.676


2.470

0.815

97.
552


0.705

1.037

99.291


1.047

1.333

98.949


2.997

2.074

97.016



EER

equal

to

3.318

%

at the

threshold

T
o

= 0.9586.

Therefore,
the

system

can

achi
e
v
e

higher

accura
c
y at

the

RMF

finger
compared

with

the

other

finger types.

Final
l
y
,

T
able

1

sh
o
ws
the

F
AR

and

FRR

with

percentage using

LI
F
,

LM
F
,

RIF

and
RMF

at

deferent

thresholds.



E.

Multi
-
modal

system

test

r
esults

A

ro
b
ust

identification

system may

require fusion

of

s
e
v
eral
finger

types

for

the

reason

that

the

limitation

presente
d

in

one
finger

may

be

compensated

by

another

finge
r
.

The

goal

of

this
e
xperiment
w
as

to

i
n
v
esti
g
ate the

systems

performance when
we

fuse

information

from

s
e
v
eral

finger

types

of

a

person.
In

f
act,

at

such

a

case

the

system
w
orks

as

a

kind

of

multi
-

modal

system

with

a

single

biometric trait

b
ut

multiple

units.
Therefore,

information presented

by

di
f
ferent

biometrics

(LI
F
,
LM
F
,

RIF

and

RMF)

is

fused

to

ma
k
e

the

system

e
f
ficient.

1)

Fusion

at

mat
c
hing sco
r
e

l
e
vel:

Fusion

at

the

matching
-

score

l
e
v
el

is

prefe
rred in

the

field

of

biometrics

because

there
is

su
f
ficient information

content

and

it

is

easy

to

access

and
combine

the

matching

scores

[12]. At

the

matching

score l
e
v
el
fusion,

the

matching scores

output

by

multiple

matchers (sub
-

system)

are

int
e
grated.

In

our

system,

di
f
ferent

combinations
of

finger types

and

di
f
ferent

fusion

rules,

such

as

Sum
-

sco
r
e

(
SU
M
),

Min
-
sco
r
e
(
MI
N
),

Max
-
sco
r
e
(
MA
X
),

Mul
-
sco
r
e
(
MU
L
)

and

Sum
-
weighting

sco
r
e

(
WHT

),

were

tested

to

find
the

combination that

optimizes

the

s
ystem

accura
c
y
.

Thus,

to
find

the

better

of

the

all

fusion

rules

and

combinations,

with

the
l
o
west

EER,

T
able

2

ta
b
ulates EER

for

v
arious

combinations
and

fusion rules.

As

can

be

seen,

the

best

result

w
as

obtained
with

the

combination

of

all

fingers and

th
e

fusion

rule

w
as
MUL

rule,

it

can

achi
e
v
e

e
v
en

higher

precision,

an

EER

of

0.370

%

and

a

T
o

of

0.9190.

The

performance of

the

open

set
identification system

is

significantly

impr
ov
ed

by

using

the
fusion

and

it

is

comparable with

other

hand

based

biometr
ics,
such

as

hand

geometry

and

fingerprint

identification

[13],

[14].

2)

Fusion

at

decision

l
e
vel:

Designing

a

suitable

method
of

decision

combinations is

a

k
e
y

point

for

the

ensembles
performance. In

this

pape
r
,

a

simplest

method

for

the

v
oting
schemes,

plu
r
ality

vote
,

is

used

[15].

H
o
w
e
v
e
r
,

in

this

method,
just

count

the

number

of

decision

for

each

class

and

assign
the

sample

to

the

class

that

obtained the

highest

number

of
v
otes.

Note

that,

the

number

of

sub
-
systems

should

be

ab
ov
e

or

equal

to

3.

F
or

t
he

ev
aluation

of

the

system

performance,
in

the

case

of

multi
-
modal system

based

on

decision

l
e
v
el,

a
series

of

e
xperiments were

carried

out

using

a

di
f
ferent

finger
type

combinations and

the

results

are

sh
o
wn

in

T
able

3.

From
T
able

3,

it

can

be

seen

that

our

identification

system

achi
e
v
es
a

best

performance

when

using

RI
F
,

RMF

and

LIF

(
F
AR

=

0.705

%,

FRR

=

1.037%

and

GAR

=

99.291%).

Finall
y
,

in

Fig.

5
.
(a)
,

we

compare

the

performance of
di
f
ferent systems

(uni
-
modal

and

multi
-
modal

based

on

fusion
at matchin
g

score

l
e
v
el).

The

results

sh
o
w

the

benefits

of

using
the

multi
-
modal system

with

matching

score

l
e
v
el

fusion.
Therefo
r
,

the

distance

distri
b
utions of

genuine

and

imposter
matchings obtained

by

the

proposed

scheme,

if

the

all

fingers
are

fused

in

the

case

of

matching score

l
e
v
el

by

MUL

rule

and
the

results

e
xpressed

as

a

F
AR and

FRR

depending

on

the
threshold,

are

plotted

in

Fig.

5
.
(b)

and

Fig.

5
.
(c)
,

respect
i
v
el
y
.


VII
.

C
ONCLUSIO
N

AN
D

FU
R
THE
R

W
OR
K

In

this

pape
r
,

a

multi
-
modal

biometric

identification

s
ystem,
using

FKP

biometric, based

on

fusion

of

s
e
v
eral

biometric
traits,

four

finger types,

has

been

proposed.

Fusion

of

these
biometric traits

is

carried

out

at

the

matching score

l
e
v
el

and
decision

l
e
v
el.

The

proposed

system

use 2D
-
BDCT

for feature
e
xtra
cted,

GMM

for

modeling

and

log
-
li
k
elihood

for

matching
process.

T
o

compare

the

proposed

multi
-
modal

system

with

the
uni
-
modal

systems,

a

series

of

e
xperiments

has

been

performed
in

the

case

of

open

set

identification

and

it

has

been

found
that

the

proposed

multi
-
modal system

g
i
v
es

a

considerable
performance
g
ain

ov
er

the

uni
-
modal

systems.

Our

future
w
ork

will

focus

on

the

performance
ev
aluation

in

both

phases
(
v
erification

and

identification)

by

using

a

la
r
ge

size

database
and

int
e
gration

of

other

b
iometric

traits

such

as

fingerprint

or
f
ace

to

get

the

system

performances

with

a

high

accura
c
y
.


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EFERENCE
S


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e
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Zhao,

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(
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(
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(
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Fig.

5.

Multi
-
modal

system

performance

in

the

case

of

fusion

at

matching

score

l
e
v
el

(all

fingers)

with

MUL

rule.

(a)

The

comparison between

the

uni
-
modal
and

multi
-
modal

systems,

(b)

The

genuine

and

the

imposter

distri
b
ution

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(c)

The

dependen
c
y

of

the

F
AR

and

the

FRR

on

the

v
alue

of

the

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