Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
6,
No.
6,
December
2016,
pp.
3255
–
3261
ISSN:
2088-8708
3255
Combination
a
Sk
eleton
Filter
and
Reduction
Dimension
of
K
er
nelPCA-Based
on
P
almprint
Recognition
Muhammad
K
usban
1
,
Adhi
Susanto
2
,
and
Oyas
W
ah
yunggor
o
3
1,2,3
Department
of
Electrical
Engineering
and
Information
T
echnology
,
Uni
v
ersitas
Gadjah
Mada,
Indonesia
1
Department
of
Electrical
Engineering,
Uni
v
eristas
Muhammadiyah
Surakarta,
Indonesia
Article
Inf
o
Article
history:
Recei
v
ed
Jun
29,
2016
Re
vised
Oct
18,
2016
Accepted
No
v
4,
2016
K
eyw
ord:
Sk
eleton
K
ernel
PCA
P
almprint
recognition
Feature
information
EER
ABSTRA
CT
P
almprint
identification
is
part
of
biometric
recognition,
which
attracted
man
y
re-
searchers,
especially
when
fusion
with
f
ace
identification
that
will
be
applied
in
the
airport
to
hasten
kno
wing
indi
vidual
identity
.
T
o
accelerate
the
process
of
v
erifica-
tion
feature
palms,
dimens
ion
reduction
method
is
the
dominant
technique
to
e
xtract
the
feature
information
of
palms.
The
mechanism
will
boost
if
the
R
OI
images
are
processed
prior
to
get
normalize
image
enhancement.
In
this
paper
with
three
sample
input
database,
a
k
ernel
PCA
method
used
as
a
dimension
reduction
compared
with
three
others
and
a
sk
eleton
filter
used
as
a
image
enhancement
method
compared
with
six
others.
The
final
results
sho
w
that
the
proposed
method
success
fully
achie
v
e
the
tar
get
in
terms
of
the
processing
time
of
0
:
7415
second,
the
EER
performa
nce
rate
of
0.19
%
and
the
success
of
v
erification
process
about
99,82
%.
Copyright
c
2016
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Muhammad
K
usban
Departemen
T
eknik
Elektro
dan
T
eknologi
Informasi
(DTETI)
-
UGM
&
T
eknik
Elektro
Uni
v
ersitas
Muhammadiyah
Surakarta
muhammadkusban.s3te13@mail.ugm.ac.id
Muhammad.K
usban@ums.ac.id
1.
INTR
ODUCTION
P
almprint
recognition
is
part
of
the
biometric
system
which
attracted
man
y
researchers.
One
w
ay
to
optimize
the
v
erification
and
identification
is
to
impro
v
e
the
appearance
image
of
palm
R
OI
(
r
e
gion
of
inter
est
).
The
trick
is
to
use
a
proper
filter
method
to
g
ain
a
uniform
brightness
le
v
el
of
the
image
so
that
the
ne
xt
process
to
obtain
feature
information
becomes
easier
.
Another
trick
is
by
selecting
a
dimension
reduction
method
in
accordance
with
the
pattern
of
information
from
the
palm
so
that
the
ongoing
process
can
distinguish
between
the
original
pattern
and
the
f
ak
e
is
more
accurate
and
ef
ficient
in
the
term
s
of
EER
(
equal
err
or
r
ate
)
and
time.
There
are
some
researchers
who
ha
v
e
discussed
the
use
of
the
R
OI
filter
and
dimension
reduct
ion
methods
to
impro
v
e
the
palmprint
recognition.
The
filter
function,
among
others,
is
to
get
the
sharpness
of
the
image
[1]
and
also
to
access
feature
information
more
ef
fecti
v
ely
[2].
Some
researchers
ha
v
e
used
the
filter
method
in
the
field
are
the
Laplacian,
the
Gaussian,
and
the
unsharp
masking
[3].
The
method
has
a
dra
wback
that
is
by
increasing
the
noise
when
an
acquisition
process
tak
es
place.
By
W
ang
and
Leedham
to
eliminate
the
noise
is
by
using
a
median
filter
that
continued
to
use
a
2D
Gaussian
la
w
pass
[4].
Zhao
et
al.
using
a
series
of
filters,
namely:
match
filter
,
the
W
iener
,
and
smoothing
filter
that
the
o
v
erall
aims
to
ele
v
ate
signal-to-noise
ratio,
remo
v
e
noise,
and
to
impro
v
e
the
appearance
of
the
image
[5].
Some
researchers
ha
v
e
used
a
dimension
reduction
algorithm
to
obtain
an
optimal
palmprint
recogni-
tion.
In
practice,
the
feature
e
xtraction
of
image
tak
en
directly
from
a
2D
image
matrix
based
instead
of
the
v
ectors-based
on
scatter
dif
ference
criterion.
As
a
result,
let
to
the
small
sample
size
(SSS)
which
will
af
fect
the
appearance
of
the
singularity
problem.
W
an
has
conducted
research
using
two-dimensional
gr
aph
embedding
local
discrimant
analysis
(2DLGED
A)
to
o
v
ercome
the
singularity
LD
A
[6].
Ho
we
v
er
,
Xinchun
found
that
the
use
of
principal
component
analysis
(PCA)
is
the
best
selection
of
dimension
reduction
compared
with
other
J
ournal
Homepage:
http://iaesjournal.com/online/inde
x.php/IJECE
,
DOI:
10.11591/ijece.v6i6.11677
Evaluation Warning : The document was created with Spire.PDF for Python.
3256
ISSN:
2088-8708
algorithms
[7].
The
idea
w
as
reinforced
by
Imtiaz
that
for
a
more
rob
ust
palmprint
recognition,
the
use
of
PCA
done
after
the
process
of
2D
D
WT
in
adv
ance
with
the
aim
to
get
more
ef
ficient
the
local
v
ariables
in
each
se
gment
of
palm
[8].
From
all
of
the
dimension
reduction
methods
which
ha
v
e
been
used
in
the
study
,
none
of
them
has
an
optimal
performance
on
all
side
[9][10].
Therefore,
in
this
paper
of
fered
a
proposal
t
o
impro
v
e
t
he
detection
system
through
palm
print
with
K
ernel
PCA
(KPCA)
that
processed
after
the
process
sk
eleton
filter
in
all
R
OI
of
palms.
The
KPCA
is
able
to
produce
an
ef
ficient
discriminant
rate
[11].
Although
the
preliminary
research,
K
usban
[12]
stated
that
Gabor
parameters
of
8
5
and
the
dimension
reduction
of
PCA
can
g
ai
n
a
great
achie
v
ement
in
v
erifying
of
palms.
From
the
research
that
ha
v
e
been
conducted,
the
sk
eleton
filter
method
for
enhancement
image
and
the
KPCA
for
reduction
dimension,
produces
a
promising
outcome.
As
a
comparison,
the
sk
eleton
filter
compared
with
six
other
filters
and
the
KPCA
compared
with
three
other
dimension
reduction.
All
simulations
run
using
three
kinds
input
of
the
database.
2.
PR
OPOSED
METHOD
2.1.
Image
Enhancement
Under
normal
condition,
the
entire
acquisition
image
of
palms
can
not
be
directly
benefited
to
e
xtract
feature
of
palm
R
OI
because
it
contains
big
unw
anted
information
[2].
T
o
solv
e
the
problem,
image
enhance-
ment
frequently
is
used
to
g
ain
local
pattern
more
clearly
,
so
it
helps
in
strengthening
the
output
rate
of
feature
information
[13].
Some
e
xamples
of
the
use
of
v
arious
filters
to
the
R
OI
image
of
palms
visible
in
Figure
1.
Filter
sk
eleton
has
been
pre
viously
used
by
Lin
in
his
research
to
obtain
all
lines
minutiae
in
palm
[14].
The
result
is
an
ability
of
sk
eleton
filter
to
impro
v
e
the
appearance
of
images,
thus
impro
ving
sys
tem
per
-
formance
and
upgrade
the
point
pattern
matching
approach.
Ho
we
v
er
,
there
is
the
weakness
of
this
method
that
is
increasing
the
number
of
feature
information
meaningless
that
resulted
adds
to
the
comple
xity
of
the
com-
puting
process.
T
o
o
v
ercome
this
problem,
the
authors
propose
sk
eleton
filter
that
only
analyze
the
principal
lines
of
palms
by
the
threshold
method
which
generate
outcome
as
sho
wn
in
Figure
1
(e).
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure
1:
A
series
visualization
R
OI
of
palm
from
se
v
en
dif
ferent
filter
process:
(a)
Original
(b)
Anisotropic
(c)
Multiple
(d)
Shock
(e)
W
a
v
elet
(f)
Sk
eleton
(d)
Histogram
Equalization.
The
notation
of
the
sk
eleton
S
(
G
)
from
the
set
G
is
a
point
p
of
S
(
G
)
and
d
p
is
the
biggest
disk
with
center
in
p
,
then
the
G
in
this
disc
is
a
’maximum
disk’.
The
disk
d
p
touches
the
boundary
G
in
tw
o
or
more
IJECE
V
ol.
6,
No.
6,
December
2016:
3255
–
3261
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
3257
dif
ferent
places.
The
sk
eleton
of
G
is
defined
for
morphology
operation
by
the
erosion
and
opening
function.
S
(
G
)
=
K
[
k
=0
S
k
(
G
)
;
(1)
with
S
k
(
G
)
=
(
G
k
H
)
(
G
k
H
)
H
and
H
is
the
matrix
dilation
and
erotion
or
structuring
element
with
(
G
k
H
)
for
k
sequential
erosions
of
G
:
(
G
k
H
)
=
(
:
:
:
((
G
H
)
H
)
:
:
:
)
H
;
k
times,
and
K
is
the
last
iterati
v
e
process
before
G
erode
to
an
empty
v
alue,
or
K
=
max
f
k
j
(
G
k
H
)
6
=
;g
.
In
its
application,
the
filter
sk
eleton
is
approached
by
using
the
principles
distance
between
each
point
and
their
boundary
.
If
kno
wn
the
initial
x
0
(
m
1
;
n
1
)
=
x
(
m
2
;
n
2
)
are
tw
o
points
equidistant,
then
the
distance
transform
defined
x
k
(
m
2
;
n
2
)
=
x
0
(
m
1
;
n
1
)
+
min
f
x
k
1
(
i;
j
)
:
d
(
m
1
;
n
1
;
i;
j
)
1
g
(2)
The
sk
eleton
is
the
set
of
v
alue
whose
distance
from
the
nearest
boundary
is
has
maximum
in
locally
.
f
(
m
1
;
n
1
)
:
x
k
(
m
2
;
n
2
)
x
k
(
i;
j
)
;
d
(
m
1
;
n
1
;
i;
j
)
1
g
(3)
2.2.
K
er
nel
PCA
The
main
concern
in
biometric
recognition
is
the
amount
of
data
continued
increase
significantly
.
It
is
wh
y
the
dimension
reduction
method
is
absolutely
necessary
.
In
an
ef
fort
to
di
vide
the
data
i
n
t
o
smaller
,
the
PCA
method
is
widely
used
in
palmprint
recognition
[15].
Ho
we
v
er
,
the
technique
has
dra
wbacks
dif
ficult
to
ackno
wledge
the
feature
information
in
a
single
image
that
ha
v
e
a
v
ariation
orientation.
T
o
o
v
ercome
this
problem,
it
is
necessary
to
use
non-linear
method
and
k
ernel
PCA
(KPCA)
successfully
used
in
biometrics
[11].
If
it
is
kno
wn
in
the
PCA
apply
an
association
la
w
C
v
w
=
w
,
with
C
v
is
the
matrix
co
v
ariance
and
x
is
the
center
data,
then:
C
v
=
1
n
q
X
i
=1
x
i
x
0
i
(4)
under
the
condition
w
2
f
x
1
;
:
:
:
;
x
n
g
if
6
=
0
,
<
x
i
,
w
x
i
,
and
C
v
w
>
i
=
1
;
:
:
:
;
q
:
When
the
is
a
high-dimension
space,
then
the
KPCA
applies
the
same
situations.
C
v
=
1
q
P
q
i
=1
(
x
i
)(
x
i
)
T
.
F
or
k
ernel
matrix
K
with
size
q
q
will
ha
v
e
a
v
alue
of
association
k
(
x
i
;
x
j
)
=
h
(
x
i
)
;
(
x
j
)
i
,
with
the
centering
data
is
as
follo
w
^
(
x
)
=
(
x
)
s
(
x
)
=
(
x
)
1
q
q
X
i
=1
(
x
i
)
;
(5)
thus,
the
transformation
k
ernel
space
is
^
k
(
x;
z
)
=
h
^
;
^
(
z
)
i
=
*
(
x
)
1
q
q
X
i
=1
;
^
(
z
)
1
q
q
X
i
=1
(
x
i
)
+
=
k
(
x;
z
)
1
q
q
X
i
=1
k
(
x;
x
1
)
1
q
q
X
i
=1
k
(
z
;
x
i
)
+
1
q
2
q
X
i;j
=1
k
(
x
i
;
x
j
)
(6)
3.
RESEARCH
AND
DISCUSSION
Research
conducted
using
three
database
input
of
palm
image,
namely:
Casia
(
C
),
IITD
India
(
I
),
and
PolyU
(
P
).
The
number
of
data
samples,
respecti
v
ely
,
are
550,
450,
and
650
with
each
item
ha
v
e
a
v
ariety
of
image
appearance
as
much
as
5,
6,
and
10.
So
that
the
t
otal
amount
of
imagery
used
is
11.950
palm
images
that
dif
ferent
from
each
other
.
Softw
are
for
simulation
is
Matlab
R2014b
under
W
indo
ws
7
Pro.
While,
the
hardw
are
is
PC
Intel
i7
4500K
with
8
GHz
of
main
memory
.
A
Sk
eleton
F
ilter
and
K
ernelPCA
On
P
almprint
Reco
gnition
(Muhammad
K
usban)
Evaluation Warning : The document was created with Spire.PDF for Python.
3258
ISSN:
2088-8708
From
the
research
that
has
been
done
for
palmprint
recognition,
a
rate
of
research
sho
wn
in
T
able
1.
F
our
types
reduction
dimension,
namel
y
KPCA,
KF
A,
LD
A,
and
PCA
are
used
to
get
feature
information
of
palm
image
from
three
dif
ferent
databases
and
se
v
en
distinct
filter
.
The
results
process
from
se
v
enth
filter
is
sho
wn
in
Fig
1.
Finally
,
The
result
of
research
are
the
rate
process
and
the
performance
of
EER
v
alue
from
each
filters.
T
able
1:
The
results
of
using
multiple
filters
in
f
o
ur
dimension
reduction
based
(RD)
to
obtain
a
rate
of
time
process
and
rate
of
performance
(EER)
from
three
types
databases:
Casia
(
C
),
IITD
India
(
D
),
and
PolyU
(
P
)
D
R
Method
Casia
IITD-India
PolyU
T
ime
EER
V
er
.
T
ime
EER
V
er
.
T
ime
EER
V
er
.
KF
A
Original
1,8587
0,4261
0,5746
0,5868
0,3363
0,6656
1,1003
0,3011
0,6991
Anisotropic
1,7318
0,4807
0,5215
0,5742
0,3364
0,6644
1,0355
0,3771
0,6227
Multiple
1,8023
0,4175
0,5823
0,5626
0,3376
0,6622
1,1562
0,3307
0,6700
Shock
1,7188
0,4696
0,5315
0,5709
0,3998
0,6000
1,1946
0,3984
0,6009
W
a
v
elet
1,7338
0,4709
0,5292
0,5935
0,4278
0,5722
1,1024
0,4145
0,5855
Sk
eleton
1,7303
0,4446
0,5554
0,5666
0,3174
0,6822
1,1060
0,3502
0,6500
Histogram
1,7513
0,4472
0,5531
0,6010
0,3067
0,6922
1,1536
0,2787
0,7209
KPCA
Original
1,3050
0,0249
0,9754
0,4066
0,0189
0,9811
0,7514
0,0036
0,9964
Anisotropic
1,3502
0,2267
0,7739
0,4225
0,5000
0,4611
0,7368
0,5000
0,5755
Multiple
1,3572
0,5000
0,5085
0,4191
0,0200
0,9800
0,7676
0,0027
0,9973
Shock
1,2729
0,0109
0,9892
0,3978
0,0233
0,9767
0,7873
0,0073
0,9927
W
a
v
elet
1,2697
0,0663
0,9339
0,4163
0,0434
0,9567
0,7717
0,0084
0,9918
Sk
eleton
1,3039
0,0146
0,9854
0,4073
0,5000
0,4933
0,7415
0,0019
0,9982
Histogram
1,3331
0,0154
0,9846
0,4053
0,0178
0,9822
0,9600
0,0025
0,9973
LD
A
Original
4,1741
0,0123
0,9877
2,0252
0,0148
0,9856
3,1772
0,0037
0,9964
Anisotropic
4,2425
0,2405
0,7592
2,1251
0,0199
0,9800
3,1990
0,0082
0,9918
Multiple
4,3174
0,0169
0,9831
2,0290
0,0166
0,9833
3,3382
0,0046
0,9955
Shock
4,2774
0,0129
0,9869
2,0957
0,0266
0,9733
3,2568
0,0073
0,9927
W
a
v
elet
4,2314
0,0646
0,9354
2,1914
0,0557
0,9444
3,4504
0,0136
0,9864
Sk
eleton
4,2408
0,0093
0,9908
2,0870
0,0167
0,9833
3,3474
0,0036
0,9964
Histogram
4,3522
0,0094
0,9908
2,0972
0,0167
0,9833
3,3670
0,0025
0,9973
PCA
Original
3,1740
0,0347
0,9654
1,6684
0,0224
0,9778
2,0115
0,0079
0,9918
Anisotropic
3,2567
0,2732
0,7269
1,4563
0,0256
0,9744
2,1097
0,0112
0,9891
Multiple
3,3113
0,0308
0,9692
1,4778
0,0244
0,9756
2,1430
0,0064
0,9936
Shock
3,3131
0,0324
0,9677
1,4440
0,0256
0,9744
2,2786
0,0091
0,9909
W
a
v
elet
3,1949
0,1033
0,8969
1,4141
0,0656
0,9344
2,0819
0,0199
0,9800
Sk
eleton
3,2092
0,0215
0,9785
1,4256
0,0201
0,9800
2,2206
0,0046
0,9955
Histogram
3,2495
0,0223
0,9777
1,4204
0,0210
0,9789
2,1732
0,0046
0,9955
The
v
alues
listed
in
the
T
able
1
directly
sho
w
that
the
sk
eleton
filter
is
superior
at
the
time
consumed
compared
others
on
three
dif
ferent
databases.
It
is
seen
that
the
use
of
sk
eleton
contains
the
same
kind
balanced
line
thickness
with
the
color
of
white
in
the
background.
While
the
other
type
has
lines
dif
ferent
thickness
with
dark,
distinct
gradation
of
the
background.
In
Figure
2
sho
w
some
of
the
performance
curv
e
R
OC
(the
recei
v
er
operating
characteristic),
CMC
(the
cumulati
v
e
match
curv
e),
DET
(the
detection
error
tradeof
f),
and
EPC
(the
e
xpected
performance
curv
e).
Seen
from
the
display
of
four
curv
es,
the
sk
eleton
filter
with
a
yello
w
color
representation
dominate
other
types.
Although
the
R
OC
curv
e
and
the
DET
in
Figure
2
(a)
and
(c)
the
sk
eleton
inferior
to
shock
filter
,
b
ut
e
v
entually
the
method
has
best
v
alue
and
the
statement
is
reinforced
with
a
vie
w
EPC
curv
e
sho
wn
in
Figure
2
(d)
that
the
yello
w
color
is
the
bottom
line
or
has
an
error
rate
of
the
smallest.
The
ne
xt
process
is
the
selection
method
of
dimension
reduction
after
all
R
OI
image
of
palms
filtered
by
the
sk
eleton
method.
A
KPCA
(the
k
ernel
principal
component
analysis)
method
has
the
outstanding
per
-
formance
compared
with
other
reduction
dimension
such
as:
LD
A
(linear
discriminant
analysis
),
KF
A
(k
ernel
fisher
analysis)
and
PCA
(princi
pal
component
analysis)
as
seen
in
the
Figure
3.
The
KPCA
method
with
black
color
lines
appear
to
ha
v
e
the
most
e
xcellent
display
of
curv
e
performance
R
OC,
CMC,
DET
,
and
EPC.
Espe-
cially
for
R
OC
in
Figure
3
(a),
where
the
black
color
lines
is
in
the
top
position
curv
e
so
that
the
f
alse
rejection
IJECE
V
ol.
6,
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6,
December
2016:
3255
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3261
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IJECE
ISSN:
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3259
T
able
2:
Performance
of
proposed
system
and
other
systems
No.
T
ype
EER
(%)
1.
V
arious
noise
density
le
v
els
of
palms
on
phase-dif
ference
information,
[16]
5.8475
2.
W
a
v
elet
combination
on
ne
w
w
a
v
elet
based
method,
[17]
4.0702
3.
Fused
on
SIFT
-based
Image
Alignment,
[18]
0.4846
&
0.5078
(left-right)
4.
PV
-Full-1.0
by
FVC-onGoing
on
Latent
P
almprint
Matching,
[19]
5.6
5.
EER
on
Multifeature-Based
High-Resolution,
[20]
4.8
6.
Proposed
approach
0.19
rate
has
the
highest
v
alue
or
close
to
1.
While
the
EPC
curv
e
in
Fi
gure
3
(d)looks
the
black
color
lines
to
be
in
the
lo
west
position
error
rate
or
highest
percentage
of
v
erification.
The
statement
reinforced
by
e
vidence
from
T
able
1
that
the
use
of
KPCA
method
e
v
en
rob
ust
since
data
input
increasing
in
number
ranging
from
small
to
lar
ger
that
is
IITD
India,
Casia,
and
then
PolyU.
F
rom
the
output
research,
it
yields
better
performance
in
terms
of
error
equal
rate
(EER)
when
compared
with
other
similar
studies
as
sho
wn
in
T
able
2.
False Accept Rate
1
0
−
6
1
0
−
4
1
0
−
2
1
0
0
False Rejection Rate
0
0
.
2
0
.
4
0
.
6
0
.
8
1
Original
Anisotropic
Multiple
Shock
Skeleton
Wavelet
Histogram
(a)
Rank
0
.
6
5
0
.
7
0
.
7
5
0
.
8
0
.
8
5
0
.
9
0
.
9
5
1
Recognition Rate
0
10
0
20
0
30
0
40
0
50
0
Original
Anisotropic
Multiple
Shock
Skeleton
Wavelet
Histogram
(b)
False Alarm proba
bility (in %)
1
0
−
3
1
0
−
2
1
0
−
1
1
0
0
Miss probability (in %)
0
0
.
2
0
.
4
0
.
6
0
.
8
1
Original
Anisotropic
Multiple
Shock
Skeleton
Wavelet
Histogram
(c)
Alpha
1
0
−
1
1
0
0
Error rate
0
.
0
2
0
.
0
4
0
.
0
6
0
.
0
8
0
.
1
0
.
1
2
0
.
1
4
Original
Anisotropic
Multiple
Shock
Skeleton
Wavelet
Histogram
(d)
Figure
2:
Curv
es
are
used
to
sho
w
the
achie
v
ement
of
v
arious
the
filter
method
in
R
OI
image
including
the
type
of
performance
to:
(a)
R
OC
(b)
CMC
(c)
DET
(d)
EPC.
4.
CONCLUSION
Globally
,
the
sk
eleton
method
has
the
best
performance
compared
to
other
filters:
original,
anisot
ropic,
multiple,
shock,
w
a
v
elet,
and
histogram
with
the
highest
v
alue
is
99,82
%
in
successfully
v
erification
process
with
error
rate
about
0,19
%
in
PolyU
database
and
KPCA-based.
Ov
erall
the
KPCA
is
the
most
suitable
method
of
dimension
reduction
to
obtain
the
feature
from
v
erification
and
identification
of
palms.
A
Sk
eleton
F
ilter
and
K
ernelPCA
On
P
almprint
Reco
gnition
(Muhammad
K
usban)
Evaluation Warning : The document was created with Spire.PDF for Python.
3260
ISSN:
2088-8708
False Accept Rate
1
0
−
6
1
0
−
4
1
0
−
2
1
0
0
False Rejection Rate
0
0
.
2
0
.
4
0
.
6
0
.
8
1
KFA
KPCA
LDA
PCA
(a)
Rank
1
0
−
3
1
0
−
2
1
0
−
1
1
0
0
Recognition Rate
0
1
0
0
2
0
0
3
0
0
4
0
0
5
0
0
KFA
KPCA
LDA
PCA
(b)
False Alarm probability (in %)
1
0
−
3
1
0
−
2
1
0
−
1
1
0
0
Miss probability (in %)
0
0
.
2
0
.
4
0
.
6
0
.
8
1
KFA
KPCA
LDA
PCA
(c)
Alpha
1
0
−
1
1
0
0
Error rate
0
0
.
2
0
.
4
0
.
6
0
.
8
1
KFA
KPCA
LDA
PCA
(d)
Figure
3:
Curv
es
are
used
to
sho
w
the
achie
v
ement
of
v
arious
the
dimension
reduction
method
in
palmprint
recognition
including
the
type
of
performance
to:
(a)
R
OC
(b)
CMC
(c)
DET
(d)
EPC.
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