T
E
L
K
O
M
N
I
K
A
T
elec
o
m
m
un
ica
t
io
n,
Co
m
pu
t
ing
,
E
lect
ro
nics
a
nd
Co
ntr
o
l
Vo
l.
19
,
No
.
2
,
A
p
r
il
2
0
2
1
,
p
p
.
4
3
2
~
4
3
7
I
SS
N:
1693
-
6
9
3
0
,
ac
cr
ed
ited
First Gr
ad
e
b
y
Kem
en
r
is
tek
d
i
k
ti,
Dec
r
ee
No
: 2
1
/E/KPT
/2
0
1
8
DOI
: 1
0
.
1
2
9
2
8
/TE
L
KOM
NI
K
A.
v
1
9
i2
.
1
6
5
7
2
432
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//jo
u
r
n
a
l.u
a
d
.
a
c.
id
/in
d
ex
.
p
h
p
/TELK
OM
N
I
K
A
Ea
rprint
re
co
g
nit
io
n using
deep
learning
t
ech
nique
Arw
a
H
.
Sa
lih
H
a
m
da
ny
1
,
A
s
ee
l Th
a
m
a
r
E
bra
hem
2
,
Ah
m
ed
M
.
Alk
a
ba
bji
3
1,
2
De
p
a
rtme
n
t
o
f
C
o
m
p
u
ter E
n
g
i
n
e
e
rin
g
,
No
r
th
e
rn
Tec
h
n
ica
l
Un
iv
e
rsity
M
o
su
l,
Ira
q
3
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter E
n
g
in
e
e
rin
g
,
U
n
iv
e
rsit
y
o
f
M
o
su
l,
Ira
q
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ma
y
1
,
2
0
2
0
R
ev
is
ed
Oct
2
5
,
2
0
2
0
Acc
ep
ted
No
v
1
1
,
2
0
2
0
Earp
rin
t
h
a
s
in
tere
stin
g
ly
b
e
e
n
c
o
n
sid
e
re
d
f
o
r
re
c
o
g
n
it
i
o
n
s
y
ste
m
s.
It
re
fe
rs
to
th
e
s
h
a
p
e
o
f
e
a
r,
wh
e
re
e
a
c
h
p
e
rso
n
h
a
s
a
u
n
iq
u
e
sh
a
p
e
o
f
e
a
rp
rin
t.
It
is
a
stro
n
g
b
i
o
m
e
tri
c
p
a
tt
e
rn
a
n
d
i
t
c
a
n
e
ffe
c
ti
v
e
ly
b
e
u
se
d
f
o
r
a
u
t
h
e
n
ti
c
a
ti
o
n
s.
In
th
is
p
a
p
e
r,
a
n
e
ffi
c
ien
t
d
e
e
p
lea
rn
in
g
(DL)
m
o
d
e
l
fo
r
e
a
rp
rin
t
re
c
o
g
n
it
io
n
is
d
e
sig
n
e
d
.
T
h
is
m
o
d
e
l
is
n
a
m
e
d
th
e
d
e
e
p
e
a
rp
ri
n
t
lea
rn
in
g
(DEL
).
It
is
a
d
e
e
p
n
e
two
rk
th
a
t
c
a
re
fu
ll
y
d
e
sig
n
e
d
f
o
r
se
g
m
e
n
ted
a
n
d
n
o
rm
a
li
z
e
d
e
a
r
p
a
tt
e
rn
s.
IIT
De
lh
i
e
a
r
d
a
tab
a
se
(IIT
DED) v
e
rsio
n
1
.
0
h
a
s
b
e
e
n
e
x
p
lo
it
e
d
i
n
th
is
stu
d
y
.
Th
e
b
e
st
o
b
tai
n
in
g
a
c
c
u
ra
c
y
o
f
9
4
%
is r
e
c
o
rd
e
d
fo
r
th
e
p
ro
p
o
se
d
DEL.
K
ey
w
o
r
d
s
:
B
io
m
etr
ic
Dee
p
lear
n
in
g
E
ar
p
r
in
t
R
ec
o
g
n
itio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ar
wa
H.
Salih
Ham
d
an
y
,
Dep
a
r
tm
en
t o
f
C
o
m
p
u
ter
E
n
g
i
n
ee
r
in
g
No
r
th
er
n
T
ec
h
n
ical
Un
iv
e
r
s
ity
Mo
s
u
l,
I
r
aq
E
m
ail:
ar
wah
am
id
7
8
@
n
tu
.
e
d
u
.
iq
1.
I
NT
RO
D
UCT
I
O
N
R
ec
o
g
n
izin
g
p
e
o
p
le
is
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
ta
n
t
f
ield
in
s
e
cu
r
ity
s
y
s
tem
s
.
I
t
s
tar
ts
f
r
o
m
ea
r
ly
s
tag
e
in
h
u
m
an
s
’
life
.
B
asically
,
in
d
iv
id
u
als
wer
e
s
tar
ted
to
b
e
r
ec
o
g
n
ized
b
y
u
s
in
g
th
eir
g
en
d
e
r
s
,
n
am
es,
ag
es
an
d
n
atio
n
alities
.
T
h
en
,
th
is
m
atte
r
h
as
b
ee
n
f
u
r
th
e
r
d
ev
el
o
p
ed
wh
er
e
s
p
ec
if
ic
d
o
c
u
m
en
ts
h
a
v
e
b
ee
n
estab
lis
h
ed
f
o
r
ea
ch
p
er
s
o
n
in
o
r
d
er
t
o
p
r
o
v
id
e
a
clea
r
id
en
tity
.
E
x
am
p
l
es
o
f
th
ese
d
o
c
u
m
en
ts
ar
e
p
ass
p
o
r
ts
an
d
id
e
n
tity
d
o
cu
m
e
n
ts
(
I
Ds).
C
lass
ical
r
ec
o
g
n
itio
n
s
y
s
tem
s
th
at
co
n
s
id
er
I
D
ca
r
d
s
,
p
ass
wo
r
d
an
d
p
e
r
s
o
n
al
id
en
tific
atio
n
n
u
m
b
er
(
PIN
)
ar
e
n
o
t
s
u
f
f
icien
t
f
o
r
r
eliab
le
id
en
tific
atio
n
.
B
ec
au
s
e
th
ey
ca
n
ea
s
i
ly
b
e
f
o
r
g
ed
,
f
o
r
g
o
tten
,
m
is
p
lace
d
,
s
to
len
,
o
r
s
h
a
r
ed
[
1
]
.
On
th
e
o
th
e
r
h
an
d
,
b
io
m
etr
ic
ch
ar
ac
ter
is
tics
ca
n
elec
tr
o
n
ically
an
d
au
to
m
atica
lly
r
ec
o
g
n
ize
in
d
iv
id
u
als
[
2
]
.
Gen
er
ally
,
b
i
o
m
etr
ic
ch
ar
ac
ter
is
tics
ca
n
b
e
class
if
ied
in
to
p
h
y
s
io
lo
g
ical
b
io
m
etr
ics
an
d
b
eh
av
i
o
u
r
al
b
io
m
etr
ics.
Ph
y
s
io
lo
g
ical
b
io
m
etr
ics
r
ef
e
r
to
th
e
p
h
y
s
io
lo
g
ical
ch
ar
ac
ter
is
tics
with
in
th
e
p
eo
p
le’
s
b
o
d
y
.
B
eh
av
io
u
r
al
b
io
m
etr
ics
p
o
in
ts
t
o
th
e
b
eh
a
v
io
u
r
al
ch
ar
ac
ter
is
tics
o
f
p
eo
p
le’
s
m
an
n
er
[
3
]
.
Ph
y
s
io
lo
g
ical
ch
ar
ac
ter
is
tics
ar
e
o
f
ten
m
o
r
e
r
eliab
le
an
d
ac
cu
r
ate
th
an
th
e
b
eh
a
v
io
u
r
al
ch
ar
ac
ter
is
tics
as
th
e
b
e
h
av
io
u
r
al
o
f
h
u
m
an
s
m
ay
b
e
in
f
lu
en
ce
d
b
y
th
e
e
m
o
tio
n
al
f
ee
li
n
g
s
lik
e
te
n
s
io
n
o
r
s
ick
n
ess
[
3
]
.
E
x
am
p
les
o
f
p
h
y
s
io
lo
g
ical
b
io
m
etr
ics
ar
e
ir
is
,
f
in
g
er
p
r
in
t,
f
ac
e
an
d
ea
r
p
r
i
n
t,
an
d
ex
a
m
p
les
o
f
b
eh
av
io
u
r
al
b
io
m
etr
ics
ar
e
v
o
ice,
g
ait
an
d
s
ig
n
atu
r
e
[
4
,
5
].
E
ar
p
r
in
t
is
a
ty
p
e
o
f
p
h
y
s
io
lo
g
ical
b
io
m
etr
ic.
I
t
p
r
in
cip
ally
r
ef
er
s
to
th
e
o
u
ter
ea
r
s
h
ap
e
.
I
t
d
if
f
e
r
s
b
etwe
en
h
u
m
an
s
,
twin
s
an
d
id
en
tical
tw
in
s
.
Mo
r
eo
v
e
r
,
ea
r
s
h
ap
es d
if
f
er
b
etwe
en
lef
t a
n
d
r
ig
h
t e
ar
s
[
6
]
.
Fig
u
r
e
1
s
h
o
ws
th
e
v
ar
io
u
s
ea
r
p
r
in
t f
ea
t
u
r
es.
T
h
e
aim
o
f
th
is
p
ap
e
r
is
p
r
o
p
o
s
in
g
a
DL
m
o
d
el
f
o
r
ea
r
p
r
i
n
t
r
ec
o
g
n
itio
n
.
T
h
is
m
o
d
el
is
ca
lled
th
e
d
ee
p
ea
r
p
r
in
t
lear
n
in
g
(
DE
L
)
,
th
is
m
o
d
el
u
s
in
g
Ad
a
m
o
p
tim
izatio
n
to
d
eter
m
in
e
th
e
b
est
p
ar
am
eter
s
o
f
co
n
v
o
l
u
tio
n
a
n
d
p
o
o
lin
g
lay
e
r
s
to
o
b
tain
th
e
b
est
er
r
o
r
if
co
m
p
er
e
with
o
th
er
tr
ain
in
g
o
p
tim
izatio
n
m
eth
o
d
s
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
E
a
r
p
r
in
t reco
g
n
itio
n
u
s
in
g
d
e
ep
lea
r
n
in
g
tech
n
iq
u
e
(
A
r
w
a
H.
S
a
lih
Ha
md
a
n
y
)
433
h
av
e
b
ee
n
ex
am
in
e
d
f
o
r
th
e
DE
L
n
etwo
r
k
s
u
c
h
as
s
to
ch
asti
c
g
r
a
d
ien
t
d
escen
t
with
m
o
m
e
n
tu
m
(
SGDM)
,
an
d
r
o
o
t
m
ea
n
s
q
u
ar
e
p
r
o
p
a
g
atio
n
(
R
MSPr
o
p
)
[
7
,
8
]
.
T
h
e
r
e
m
ain
in
g
s
ec
tio
n
s
ar
e
d
is
tr
ib
u
ted
as
f
o
llo
ws:
s
ec
tio
n
2
p
r
o
v
id
es
t
h
e
liter
atu
r
e
r
ev
iew
o
f
t
h
is
p
ap
er
,
s
ec
tio
n
3
d
esc
r
ib
es
th
e
DE
L
m
eth
o
d
,
s
ec
tio
n
4
d
is
cu
s
s
es
th
e
r
esu
lts
an
d
s
ec
tio
n
5
d
ec
lar
es
th
e
co
n
clu
s
io
n
.
A
lim
ited
n
u
m
b
er
o
f
s
tu
d
ies
co
n
s
id
er
e
d
th
e
e
ar
p
r
in
t
as
a
ty
p
e
o
f
r
ec
o
g
n
itio
n
in
th
e
liter
atu
r
e.
I
n
2
0
1
5
,
a
u
to
m
atic
r
ec
o
g
n
itio
n
s
y
s
tem
s
b
ased
o
n
en
s
em
b
le
o
f
lo
ca
l
an
d
g
lo
b
al
ea
r
p
tin
t
f
ea
tu
r
es
was
ex
p
lo
r
ed
,
a
p
r
o
m
is
in
g
p
er
f
o
r
m
a
n
ce
was
co
n
clu
d
e
d
f
o
r
co
n
s
id
er
in
g
b
o
th
lo
ca
l
an
d
g
lo
b
a
l
ea
r
p
r
in
t
f
ea
t
u
r
es
[
9
]
.
I
n
2
0
1
6
,
a
n
ew
f
ea
tu
r
e
ex
tr
ac
tio
n
ap
p
r
o
ac
h
was
illu
s
tr
ated
f
o
r
th
e
ea
r
g
eo
m
etr
y
r
ec
o
g
n
itio
n
.
I
n
t
h
is
ap
p
r
o
ac
h
,
b
o
th
th
e
m
in
im
u
m
a
n
d
m
a
x
im
u
m
ea
r
h
eig
h
t
lin
es
wer
e
e
m
p
lo
y
ed
,
th
en
,
t
h
r
ee
r
atio
-
b
ased
f
ea
tu
r
es
wer
e
h
i
g
h
lig
h
ted
to
en
h
a
n
ce
th
e
s
ca
le
o
f
r
o
b
u
s
tn
ess
[
10
]
.
I
n
th
e
s
am
e
y
ea
r
,
a
d
ec
is
io
n
-
m
ak
in
g
o
f
s
p
ar
s
e
co
d
in
g
-
in
d
u
ce
d
was
em
p
lo
y
e
d
with
th
e
ea
r
p
r
in
t.
I
t
was
p
r
o
v
ed
th
at
f
u
s
in
g
b
o
th
r
esid
u
als
an
d
c
o
ef
f
icien
ts
c
o
m
p
o
n
en
ts
ca
n
o
b
tain
b
et
ter
p
er
f
o
r
m
an
ce
s
[
1
1
]
.
I
n
2
0
1
8
co
m
b
i
n
ed
d
if
f
er
en
t
d
ee
p
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
m
o
d
els
an
d
an
al
y
ze
d
in
d
ep
th
th
e
e
f
f
ec
t
o
f
ea
r
im
a
g
e
q
u
ality
[
1
2
]
.
I
n
th
e
s
am
e
y
ea
r
,
a
f
r
am
ewo
r
k
o
f
ea
r
p
r
in
t
r
ec
o
g
n
itio
n
was d
escr
ib
ed
f
o
r
a
lig
h
t f
ield
im
ag
in
g
.
A
n
ew
len
s
let
lig
h
t f
ield
ea
r
d
atab
ase
(
L
L
FEDB)
m
eth
o
d
was
illu
s
tr
ated
b
y
u
tili
zin
g
th
e
r
ich
er
s
p
atio
-
an
g
u
lar
f
ea
tu
r
es
[
1
3
]
.
I
n
2
0
1
9
,
a
m
u
lti
-
m
o
d
al
b
io
m
etr
ic
r
ec
o
g
n
itio
n
m
eth
o
d
was
e
x
p
lain
ed
,
wh
er
e
ea
r
p
r
in
t
an
d
f
in
g
e
r
k
n
u
ck
le
p
r
in
t
(
FKP)
wer
e
u
s
ed
.
T
ec
h
n
iq
u
es
o
f
lo
c
al
b
in
ar
y
p
atter
n
(
L
B
P)
an
d
f
ea
tu
r
e
lev
el
f
u
s
io
n
(
FLF)
wer
e
ex
p
lo
ited
in
th
is
s
tu
d
y
[
1
4
]
.
I
n
th
e
s
am
e
y
ea
r
,
a
n
ew
ap
p
r
o
ac
h
f
o
r
a
s
in
g
le
e
ar
p
r
in
t
was
p
r
o
p
o
s
ed
.
I
t
co
n
s
is
ts
o
f
th
r
ee
p
h
ases
:
p
r
o
v
id
i
n
g
n
o
r
m
aliza
tio
n
p
r
o
c
ess
,
ap
p
ly
in
g
a
n
o
v
el
E
ig
en
ea
r
s
an
d
u
til
izin
g
n
ea
r
est
n
eig
h
b
o
u
r
class
if
ier
[
1
5
].
I
n
th
is
p
ap
er
,
ex
p
l
o
itin
g
a
DL
tech
n
iq
u
e
f
o
r
ea
r
p
r
in
t
r
ec
o
g
n
it
io
n
is
co
n
s
id
er
ed
.
T
h
er
ef
o
r
e,
a
DE
L
tech
n
iq
u
e
is
p
r
o
p
o
s
ed
an
d
e
v
alu
ated
.
Fig
u
r
e
1
.
Var
io
u
s
ea
r
p
r
i
n
t f
ea
t
u
r
es
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
is
wo
r
k
,
we
ar
e
p
r
o
p
o
s
in
g
th
e
DE
L
n
etwo
r
k
.
I
t
is
a
D
L
tech
n
iq
u
e
an
d
a
ty
p
e
o
f
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
.
I
t is d
e
s
ig
n
ed
to
ac
ce
p
t e
ar
p
r
in
t p
atte
r
n
s
.
Firstl
y
,
th
e
DE
L
n
etwo
r
k
ca
n
b
e
tr
ain
e
d
with
v
ar
io
u
s
ea
r
p
r
in
t
p
atter
n
s
th
at
ar
e
ac
q
u
ir
ed
f
r
o
m
d
if
f
e
r
en
t
p
er
s
o
n
s
.
T
h
en
,
th
e
DE
L
n
etwo
r
k
is
test
ed
f
o
r
n
ew
ea
r
p
r
in
t
p
atter
n
s
th
at
h
a
v
e
n
o
t
b
e
s
ee
n
b
ef
o
r
e
.
T
h
e
tr
ain
in
g
s
tag
e
will
b
e
r
esu
lted
b
y
p
r
o
d
u
cin
g
u
s
ef
u
l
v
alu
es
(
weig
h
ts
)
.
T
h
ese
v
alu
es
ca
n
b
e
s
to
r
ed
in
o
r
d
er
t
o
b
e
u
s
ed
lat
er
in
th
e
test
in
g
s
tag
e.
Fig
u
r
e
2
s
h
o
ws
th
e
g
en
e
r
al
DE
L
f
r
am
ewo
r
k
s
tr
u
ctu
r
e
.
D
E
L
n
etwo
r
k
co
n
s
is
ts
o
f
m
u
lti
-
lay
er
s
.
T
h
ese
ar
e:
th
e
ea
r
p
r
in
t
in
p
u
t,
c
o
n
v
o
lu
tio
n
,
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U)
lay
er
,
p
o
o
lin
g
,
f
u
lly
c
o
n
n
ec
t
ed
(
FC
)
,
s
o
f
tm
ax
an
d
class
if
icatio
n
lay
er
s
.
T
h
e
ar
ch
itectu
r
e
o
f
th
e
DE
L
n
etw
o
r
k
is
g
iv
en
in
Fig
u
r
e
3.
T
h
e
in
p
u
t
la
y
er
is
a
d
ap
ted
f
o
r
t
h
e
ea
r
p
r
in
t
p
atter
n
s
.
I
t
ac
ce
p
ts
g
r
ay
s
ca
le
two
-
d
im
en
s
i
o
n
a
l (
2
D)
i
m
ag
es.
T
h
u
s
,
ea
ch
ea
r
p
r
in
t im
ag
e
E
in
v
o
lv
es o
n
l
y
o
n
e
c
h
an
n
el.
R
eg
ar
d
in
g
th
e
co
n
v
o
lu
tio
n
lay
er
,
th
e
in
p
u
t
im
ag
e
E
is
tr
an
s
f
o
r
m
ed
in
to
g
r
o
u
p
o
f
f
ea
tu
r
e
m
ap
s
.
T
h
e
f
ea
tu
r
e
m
ap
s
ar
e
co
n
v
o
lv
e
d
i
n
p
u
t
im
a
g
e
b
y
a
k
er
n
el
weig
h
ts
m
atr
ix
.
T
h
e
f
o
llo
win
g
e
q
u
atio
n
r
ep
r
esen
ts
th
e
co
n
v
o
l
u
tio
n
p
r
o
ce
s
s
o
f
th
e
c
o
n
v
o
lu
tio
n
la
y
er
[
1
6
]:
,
,
=
+
∑
∑
∑
+
ℎ
,
+
,
−
1
+
,
+
,
−
1
−
1
−
1
=
1
=
−
ℎ
=
−
ℎ
(
1
)
wh
er
e:
,
,
is
co
n
v
o
lu
tio
n
lay
er
o
u
tp
u
t,
(
,
)
is
th
e
c
o
o
r
d
i
n
ate
o
f
a
s
p
ec
if
ic
p
i
x
el,
ke
h
an
d
ke
w
ar
e
th
e
k
er
n
els
o
f
h
eig
h
t
an
d
wid
th
,
r
esp
ec
tiv
ely
,
is
a
b
ias,
is
th
e
ch
an
n
el
o
f
a
s
p
ec
if
ic
lay
er
,
,
,
−
1
is
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t
E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
2
,
Ap
r
il 2
0
2
1
:
4
3
2
-
43
7
434
k
er
n
el
p
ar
a
m
eter
,
an
d
an
d
−1
ar
e
cu
r
r
e
n
t
an
d
p
r
ev
io
u
s
lay
er
s
,
r
esp
ec
tiv
ely
.
R
eL
U
p
r
o
ce
s
s
ca
n
b
e
d
escr
ib
ed
b
y
th
e
f
o
llo
win
g
eq
u
atio
n
[
17
]
:
,
,
=
m
ax
(
0
,
,
,
)
(
2
)
wh
er
e
,
,
is
a
R
eL
U
o
u
tp
u
t
?
Su
b
s
eq
u
en
tly
,
th
e
p
o
o
lin
g
la
y
er
f
u
r
th
e
r
d
ec
r
ea
s
es
th
e
s
izes
o
f
p
r
e
v
io
u
s
ch
an
n
els.
T
h
e
f
o
llo
win
g
e
q
u
at
io
n
r
ep
r
esen
ts
th
e
p
o
o
lin
g
co
m
p
u
tatio
n
[
18
]:
,
,
=
×
ℎ
+
,
×
+
,
0
≤
<
ℎ
,
0
≤
<
o
p
e
(
3
)
wh
er
e
,
,
is
a
p
o
o
lin
g
o
u
tp
u
t,
0
≤
<
ℎ
,
ℎ
is
a
p
o
o
led
ch
an
n
el
h
ei
g
h
t,
0
≤
<
,
is
a
p
o
o
led
ch
a
n
n
el
wid
th
,
0
≤
z
<
=
−
1
,
is
a
p
o
o
led
ch
an
n
el
m
atr
ix
,
o
p
e
is
th
e
m
ax
im
u
m
o
p
er
at
io
n
,
p
h
is
a
s
u
b
-
ch
an
n
el
h
eig
h
t
an
d
p
w
is
a
s
u
b
-
c
h
an
n
el
wid
th
.
H
en
ce
,
FC
lay
er
ca
n
m
atch
b
e
twee
n
th
e
p
o
o
lin
g
n
eu
r
o
n
s
an
d
r
e
q
u
ir
ed
r
ec
o
g
n
iz
in
g
s
u
b
jects.
T
h
e
f
o
llo
win
g
e
q
u
atio
n
d
em
o
n
s
tr
ates th
e
FC
o
p
er
atio
n
[
19
]
:
=
∑
∑
∑
,
,
,
,
(
)
,
3
−
1
=
1
2
−
1
=
1
1
−
1
=
1
,
∀
1
≤
≤
(
4
)
wh
er
e
is
a
FC
o
u
tp
u
t,
1
−
1
is
th
e
p
r
io
r
ch
an
n
el
h
eig
h
t
o
f
,
2
−
1
is
t
h
e
p
r
io
r
ch
an
n
el
wid
th
,
3
−
1
is
th
e
n
u
m
b
er
o
f
p
r
i
o
r
ch
an
n
els,
,
,
,
,
is
a
co
n
n
ec
tio
n
weig
h
t
b
etwe
en
FC
an
d
p
o
o
lin
g
,
O
z
is
th
e
v
ec
to
r
/v
ec
to
r
s
o
f
p
o
o
lin
g
lay
er
o
u
tp
u
ts
,
an
d
is
th
e
n
u
m
b
er
o
f
r
e
q
u
ir
e
d
cla
s
s
es.
No
w,
So
f
tm
ax
tr
an
s
f
er
f
u
n
ctio
n
ca
n
b
e
co
m
p
u
ted
as f
o
llo
ws [
2
0
,
2
1
]:
=
ex
p
(
)
∑
ex
p
(
)
−
1
=
1
(
5
)
wh
er
e
is
a
s
o
f
tm
ax
o
u
tp
u
t?
Her
ea
f
ter
,
th
e
class
ificatio
n
lay
er
is
r
eq
u
ir
ed
.
T
h
e
f
o
llo
win
g
eq
u
atio
n
ca
n
b
e
co
n
s
id
er
ed
[
2
]
:
D
r
=
{
1
=
0
ℎ
,
=
1
,
2
,
3
,
…
(
6
)
wh
er
e
D
r
is
a
class
ificatio
n
o
u
tp
u
t,
ma
x
is
o
b
v
io
u
s
ly
a
m
a
x
im
u
m
b
r
v
al
u
e
an
d
m
is
th
e
n
u
m
b
er
o
f
class
es.
Fig
u
r
e
2
.
Gen
e
r
al
DE
L
f
r
a
m
e
wo
r
k
s
tr
u
ctu
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
E
a
r
p
r
in
t reco
g
n
itio
n
u
s
in
g
d
e
ep
lea
r
n
in
g
tech
n
iq
u
e
(
A
r
w
a
H.
S
a
lih
Ha
md
a
n
y
)
435
Fig
u
r
e
3
.
T
h
e
ar
ch
itectu
r
e
o
f
t
h
e
p
r
o
p
o
s
ed
DE
L
n
etwo
r
k
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
First
o
f
all,
ea
r
p
r
i
n
t
d
atab
ase
ar
e
av
ailab
le
f
o
r
th
e
I
I
T
DE
D
v
er
s
io
n
1
.
0
.
I
t
co
n
s
is
ts
o
f
to
u
ch
less
ea
r
p
r
in
t
im
a
g
es.
I
t
was
co
llect
ed
f
r
o
m
s
tu
d
en
ts
an
d
s
taf
f
f
r
o
m
th
e
I
I
T
Delh
i
in
I
n
d
ia.
I
t
wa
s
ac
q
u
ir
ed
b
etwe
en
Octo
b
er
2
0
0
6
to
J
u
n
e
2
0
0
7
.
E
ar
p
r
in
t
im
a
g
es
wer
e
ca
p
t
u
r
e
d
u
s
in
g
i
n
d
o
o
r
e
n
v
ir
o
n
m
en
t
a
n
d
s
im
p
le
im
a
g
in
g
s
etu
p
.
2
2
1
p
e
r
s
o
n
s
in
t
h
e
ag
e
b
etwe
en
1
4
to
5
8
y
ea
r
s
wer
e
p
ar
ticip
ated
with
m
u
ltip
le
im
a
g
e
s
am
p
les
(
at
least
th
r
ee
ea
r
p
r
i
n
ts
)
.
All
ea
r
p
r
in
t
im
ag
es
ar
e
o
f
r
eso
lu
tio
n
2
7
2
2
0
4
p
ix
els
an
d
t
h
ey
ar
e
o
f
ty
p
e
jp
eg
f
o
r
m
at.
Fu
r
th
er
m
o
r
e
,
s
eg
m
en
ted
ea
r
p
r
in
t
im
ag
es
ar
e
also
a
v
ailab
le
with
in
th
e
s
am
e
d
ata
b
ase,
e
ac
h
with
a
s
ize
o
f
50
1
8
0
p
ix
els
[
2
2
,
2
3
]
.
T
h
e
s
eg
m
en
ted
ea
r
p
r
i
n
t
im
ag
es
o
f
t
h
e
I
I
T
DE
D
v
er
s
io
n
1
.
0
h
as
b
e
en
em
p
lo
y
ed
in
th
is
p
ap
er
b
u
t
f
o
r
in
p
u
t
im
ag
e
s
ize
o
f
1
8
0
5
0
p
ix
els
.
T
wo
th
ir
d
n
u
m
b
er
o
f
ea
r
p
r
in
t
s
am
p
les
h
as
b
ee
n
u
s
ed
in
th
e
tr
ain
in
g
s
tag
e.
W
h
er
ea
s
,
1
0
0
ev
alu
ated
ca
s
e
h
as
b
ee
n
u
s
ed
in
th
e
test
in
g
s
tag
e.
T
h
e
t
r
ain
i
n
g
p
ar
a
m
eter
s
h
av
e
b
ee
n
s
et
as:
Ad
a
p
tiv
e
m
o
m
e
n
t
esti
m
atio
n
(
Ad
a
m
)
o
p
tim
iz
er
,
m
ax
i
m
u
m
ep
o
c
h
s
eq
u
al
5
0
,
in
itial
lear
n
r
ate
eq
u
al
0
.
0
0
0
1
,
d
ec
ay
r
ate
o
f
g
r
ad
ien
t m
o
v
in
g
av
er
a
g
e
o
f
0
.
9
,
d
ec
ay
r
ate
o
f
s
q
u
ar
ed
g
r
ad
ien
t
m
o
v
in
g
a
v
er
ag
e
o
f
0
.
9
9
9
an
d
d
en
o
m
in
ato
r
o
f
f
s
et
o
f
1
0
-
8
.
T
o
d
eter
m
in
e
th
e
b
est
DE
L
n
etwo
r
k
p
ar
am
eter
s
,
m
a
n
y
ex
p
er
im
en
ts
wer
e
ex
ec
u
ted
an
d
e
v
alu
ated
.
T
ab
le
1
s
h
o
ws v
ar
io
u
s
DE
L
n
e
two
r
k
ex
p
e
r
im
en
ts
with
Ad
am
o
p
tim
izatio
n
to
d
eter
m
in
e
th
e
b
est p
ar
am
eter
s
o
f
co
n
v
o
l
u
tio
n
an
d
p
o
o
lin
g
lay
er
s
.
I
n
th
is
tab
le,
co
n
v
o
lu
tio
n
la
y
er
a
n
d
p
o
o
lin
g
lay
er
p
ar
am
et
er
s
ar
e
e
v
alu
ated
b
y
ch
an
g
in
g
a
s
in
g
le
p
ar
am
eter
an
d
f
ix
in
g
th
e
v
alu
es
o
f
all
th
e
r
em
ain
i
n
g
p
ar
am
eter
s
.
T
h
e
DE
L
n
etwo
r
k
p
er
f
o
r
m
an
ce
ca
n
s
im
p
ly
b
e
e
v
alu
ated
b
y
th
e
o
b
tain
ed
ac
cu
r
ac
y
.
I
t
ca
n
b
e
o
b
s
er
v
ed
th
at
t
h
e
b
est
c
o
n
v
o
lu
tio
n
lay
er
p
ar
am
eter
s
ar
e
r
ec
o
d
e
d
f
o
r
:
f
ilter
s
ize
o
f
1
3
1
3
,
n
u
m
b
er
o
f
f
ilter
s
eq
u
al
1
0
an
d
p
ad
d
in
g
o
f
0
.
Fu
r
th
er
m
o
r
e
,
it c
an
b
e
s
ee
n
th
at
th
e
b
est p
o
o
lin
g
lay
er
p
ar
a
m
eter
s
ar
e
r
ep
o
r
ted
f
o
r
: p
o
o
lin
g
ty
p
e
o
f
m
ax
im
u
m
,
f
ilter
s
ize
o
f
5
5,
s
tr
id
e
o
f
1
0
an
d
p
ad
d
in
g
o
f
0
.
T
h
is
is
b
ec
au
s
e
th
e
DE
L
ac
cu
r
ac
y
af
ter
u
s
in
g
th
ese
p
ar
am
eter
s
h
as
b
en
c
h
m
ar
k
e
d
its
h
ig
h
est
v
alu
e
o
f
9
4
%.
T
u
n
in
g
an
y
p
ar
am
eter
v
alu
e
f
o
r
s
lig
h
tly
m
o
r
e
o
r
less
th
an
th
e
r
ec
o
r
d
e
d
p
ar
am
ete
r
w
o
u
ld
d
ec
r
ea
s
e
th
e
ac
cu
r
a
cy
v
a
lu
e.
T
h
e
tr
ain
in
g
c
u
r
v
es
o
f
t
h
e
DE
L
n
etwo
r
k
b
y
u
s
in
g
th
e
b
est
o
b
tain
in
g
p
a
r
am
et
er
s
ar
e
g
iv
en
in
Fig
u
r
e
4.
Mo
r
e
o
v
er
,
d
if
f
er
en
t
tr
ain
in
g
o
p
tim
izatio
n
m
eth
o
d
s
h
av
e
b
ee
n
ex
am
i
n
ed
f
o
r
t
h
e
DE
L
n
etwo
r
k
as
g
iv
en
in
T
ab
le
2.
I
n
t
h
is
tab
le,
d
if
f
er
en
t
tr
ai
n
in
g
o
p
tim
izatio
n
s
h
av
e
b
ee
n
e
v
alu
ated
.
T
h
ese
ar
e:
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
with
m
o
m
en
t
u
m
(
SGDM)
,
r
o
o
t
m
ea
n
s
q
u
ar
e
p
r
o
p
a
g
atio
n
(
R
MSPr
o
p
)
an
d
Ad
am
.
Ob
v
i
o
u
s
ly
,
Ad
am
o
p
tim
izatio
n
h
as
attain
ed
b
est
ac
cu
r
ac
y
o
f
9
4
%
c
o
m
p
ar
ed
with
th
e
SGDM
an
d
R
MSPr
o
p
as
th
ey
attain
ed
7
1
%
an
d
9
3
%,
r
esp
ec
tiv
ely
as
s
ee
in
Fig
u
r
e
4.
I
t
is
wo
r
th
m
en
tio
n
in
g
th
at
t
h
e
p
er
f
o
r
m
an
ce
o
f
in
cr
ea
s
in
g
th
e
DE
L
ar
ch
itectu
r
e
b
y
ad
d
in
g
m
o
r
e
th
an
o
n
e
co
n
v
o
lu
tio
n
,
R
eL
U
an
d
p
o
o
l
in
g
lay
er
s
ar
e
also
in
v
esti
g
ated
.
T
h
at
is
,
b
y
u
s
in
g
two
s
eq
u
en
tial
co
n
v
o
l
u
tio
n
s
,
R
eL
U,
p
o
o
lin
g
,
FC
,
So
f
tm
ax
an
d
class
if
icatio
n
lay
er
s
th
e
ac
cu
r
ac
y
d
ec
r
ea
s
ed
to
8
2
%.
Als
o
,
b
y
u
s
in
g
co
n
v
o
lu
tio
n
_
1
,
R
eL
U_
1
,
co
n
v
o
lu
tio
n
_
2
,
R
eL
U_
2
,
p
o
o
lin
g
,
FC
,
So
f
tm
a
x
a
n
d
class
if
icatio
n
lay
er
s
th
e
ac
cu
r
ac
y
d
ec
r
ea
s
ed
to
8
3
%.
Acc
o
r
d
i
n
g
to
t
h
ese
r
esu
lts
th
e
r
ep
o
r
ted
ac
cu
r
ac
ies
ar
e
to
o
f
ar
f
r
o
m
th
e
b
est
attain
ed
a
cc
u
r
ac
y
.
T
h
u
s
,
it
is
n
o
t
wo
r
th
to
in
cr
ea
s
e
th
e
co
m
p
lex
ity
o
f
th
e
DE
L
n
etw
o
r
k
a
r
ch
itectu
r
e.
T
h
e
DE
L
n
etwo
r
k
h
as
b
ee
n
co
m
p
a
r
ed
with
s
tate
-
of
-
th
e
-
ar
t
m
eth
o
d
s
as sh
o
wn
in
T
ab
le
3
.
Fro
m
T
ab
le
3
,
it c
an
clea
r
ly
b
e
s
ee
n
th
at
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
h
as a
ch
iev
ed
th
e
b
est
ac
cu
r
ac
y
o
f
9
4
%
o
v
er
s
t
ate
-
of
-
th
e
-
a
r
t
m
eth
o
d
s
af
ter
a
p
p
ly
in
g
t
h
eir
p
r
o
p
o
s
ed
ar
c
h
itectu
r
es
to
o
u
r
d
ata.
T
h
at
is
,
th
e
p
r
o
p
o
s
ed
C
NN
ar
ch
itectu
r
e
in
[
2
4
]
ac
h
iev
e
d
6
3
%
an
d
th
e
n
o
v
el
d
ee
p
f
i
n
g
er
tex
tu
r
e
lear
n
i
n
g
(
DFR
L
)
ar
ch
itectu
r
e
in
[
2
5
]
ac
h
iev
ed
7
2
%.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t
E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
2
,
Ap
r
il 2
0
2
1
:
4
3
2
-
43
7
436
T
ab
le
1
Var
io
u
s
DE
L
n
etwo
r
k
ex
p
er
im
en
ts
with
Ad
am
o
p
ti
m
izatio
n
to
d
eter
m
in
e
th
e
b
est p
ar
am
eter
s
o
f
co
n
v
o
l
u
tio
n
an
d
p
o
o
lin
g
la
y
er
s
C
o
n
v
o
l
u
t
i
o
n
l
a
y
e
r
P
o
o
l
i
n
g
l
a
y
e
r
A
c
c
u
r
a
c
y
(
%)
F
i
l
t
e
r
si
z
e
N
o
.
o
f
f
i
l
t
e
r
s
P
a
d
d
i
n
g
Ty
p
e
M
a
x
i
m
u
m
(
M
a
x
)
o
r
A
v
e
r
a
g
e
(
A
v
e
)
S
i
z
e
S
t
r
i
d
e
P
a
d
d
i
n
g
15
15
10
0
M
a
x
5
5
5
0
86
13
13
10
0
M
a
x
5
5
5
0
94
11
11
10
0
M
a
x
5
5
5
0
85
13
13
8
0
M
a
x
5
5
5
0
89
13
13
12
0
M
a
x
5
5
5
0
86
13
13
10
0
M
a
x
3
3
3
0
87
13
13
10
0
M
a
x
7
7
7
0
84
13
13
10
0
A
v
e
5
5
5
0
89
13
13
10
1
M
a
x
5
5
5
0
83
13
13
10
2
M
a
x
5
5
5
0
86
13
13
10
0
M
a
x
5
5
5
1
85
13
13
10
0
M
a
x
5
5
5
2
87
T
ab
le
2.
Dif
f
e
r
en
t e
x
am
i
n
ed
tr
ain
in
g
o
p
tim
izatio
n
m
eth
o
d
s
f
o
r
th
e
DE
L
n
etwo
r
k
T
ab
le
3.
C
o
m
p
a
r
is
o
n
s
with
d
if
f
er
en
t state
-
of
-
th
e
-
ar
t
m
eth
o
d
s
Tr
a
i
n
i
n
g
o
p
t
i
m
i
z
a
t
i
o
n
m
e
t
h
o
d
A
c
c
u
r
a
c
y
(
%)
S
G
D
M
71
R
M
S
P
r
o
p
93
A
d
a
m
94
R
e
f
e
r
e
n
c
e
s
D
L
N
e
t
w
o
r
k
A
c
c
u
r
a
c
y
[
2
2
]
C
N
N
6
3
%
[
2
3
]
D
F
TL
7
2
%
S
u
g
g
e
s
t
e
d
m
e
t
h
o
d
D
EL
9
4
%
Fig
u
r
e
4
.
T
r
ain
in
g
c
u
r
v
es o
f
th
e
p
r
o
p
o
s
ed
DE
L
n
etwo
r
k
4.
CO
NCLU
SI
O
N
I
n
th
is
s
tu
d
y
,
we
p
r
o
p
o
s
ed
an
ef
f
icien
t
DE
L
n
etwo
r
k
m
o
d
el.
T
h
is
n
etwo
r
k
h
as
th
e
ab
ilit
y
to
r
ec
o
g
n
ize
d
if
f
e
r
en
t
ea
r
p
r
in
t
p
atter
n
s
.
T
h
e
s
u
g
g
ested
m
eth
o
d
was
in
v
esti
g
ated
a
n
d
ev
alu
a
ted
f
o
r
d
if
f
er
en
t
DL
p
ar
am
eter
s
.
I
t
r
ep
o
r
ted
b
est
r
ec
o
g
n
itio
n
ac
cu
r
ac
y
o
f
9
4
%
an
d
th
is
ca
n
b
e
co
n
s
id
er
ed
as
a
p
r
o
m
is
in
g
p
er
f
o
r
m
an
ce
.
Als
o
,
th
e
DE
L
o
u
tp
er
f
o
r
m
ed
o
th
er
s
tate
-
of
-
th
e
-
ar
t n
etwo
r
k
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
"Po
r
tio
n
s
o
f
th
e
w
o
r
k
test
ed
o
n
th
e
I
I
T
D
E
ar
Data
b
ase
v
er
s
i
o
n
1
.
0
".
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
E
a
r
p
r
in
t reco
g
n
itio
n
u
s
in
g
d
e
ep
lea
r
n
in
g
tech
n
iq
u
e
(
A
r
w
a
H.
S
a
lih
Ha
md
a
n
y
)
437
RE
F
E
R
E
NC
E
S
[
1
]
Ab
e
e
r
A.
,
e
t
a
l
.
,
“
Bio
m
e
tri
c
F
a
c
e
Re
c
o
g
n
it
i
o
n
Us
i
n
g
M
u
lt
il
in
e
a
r
P
r
o
jec
ti
o
n
a
n
d
Artifi
c
ial
I
n
telli
g
e
n
c
e
,
”
P
h
D
th
e
sis,
S
c
h
o
o
l
o
f
En
g
in
e
e
ri
n
g
,
Ne
wc
a
stle Un
iv
e
rsity
,
UK
,
2
0
1
3
.
[
2
]
R.
R.
Al
-
Nim
a
,
“
S
ig
n
a
l
P
r
o
c
e
ss
in
g
a
n
d
M
a
c
h
in
e
Lea
rn
in
g
Tec
h
n
iq
u
e
s
f
o
r
Hu
m
a
n
Ve
rifi
c
a
ti
o
n
B
a
se
d
o
n
F
i
n
g
e
r
Tex
tu
re
s,”
P
h
D t
h
e
sis,
S
c
h
o
o
l
o
f
En
g
i
n
e
e
rin
g
,
Ne
wc
a
stle Un
iv
e
rsit
y
,
UK
,
2
0
1
7
.
[
3
]
V.
M
a
ty
a
s,
e
t
a
l
.
,
“
Bio
m
e
tri
c
a
u
t
h
e
n
ti
c
a
ti
o
n
s
y
ste
m
s,”
in
v
e
rfu
g
b
a
r
u
b
e
r:
h
tt
p
://
g
ro
v
e
r.
I
n
fo
rm
a
ti
k
.
Un
i
-
a
u
g
sb
u
rg
.
De
/l
it
/M
M
S
e
min
a
r/Priva
c
y
/rih
a
0
0
b
i
o
me
tric
.
Pd
f
.
Cit
e
se
e
r,
2
0
0
0
.
[
4
]
M
.
Ab
d
u
ll
a
h
,
e
t
a
l
.,
“
Cro
ss
-
sp
e
c
t
ra
l
Iris
M
a
tch
in
g
fo
r
S
u
r
v
e
il
lan
c
e
Ap
p
li
c
a
ti
o
n
s,”
S
p
rin
g
e
r,
S
u
rv
e
il
l
a
n
c
e
in
Actio
n
T
e
c
h
n
o
l
o
g
ies
fo
r Civili
a
n
,
M
i
li
ta
r
y
a
n
d
Cy
b
e
r S
u
rv
e
il
la
n
c
e
,
pp.
1
0
5
-
1
2
5
,
2
0
1
7
.
[
5
]
S.
M
in
a
e
e
,
e
t
a
l
.
,
“
An
e
x
p
e
rim
e
n
tal
stu
d
y
o
f
d
e
e
p
c
o
n
v
o
l
u
ti
o
n
a
l
fe
a
tu
re
s
f
o
r
iri
s
re
c
o
g
n
it
io
n
,
”
IEE
E
sig
n
a
l
p
ro
c
e
ss
in
g
i
n
me
d
ici
n
e
a
n
d
b
i
o
lo
g
y
sy
mp
o
siu
m (
S
P
M
B)
,
p
p
1
-
6,
2
0
1
6
.
[
6
]
Niti
n
Ka
u
sh
a
l
,
e
t
a
l
.,
“
Hu
m
a
n
e
a
r
p
rin
ts:
a
re
v
iew
,
”
J
o
u
r
n
a
l
o
f
b
i
o
me
trics
a
n
d
b
i
o
sta
ti
stics
,
v
o
l
.
2
,
no.
5
,
2
0
1
1
.
[
7
]
R.
R.
Al
-
Nim
a
,
“
Hu
m
a
n
a
u
th
e
n
ti
c
a
ti
o
n
with
e
a
rp
ri
n
t
fo
r
se
c
u
re
tele
p
h
o
n
e
sy
ste
m
,
”
Ira
q
i
J
o
u
rn
a
l
o
f
Co
mp
u
ter
s,
Co
mm
u
n
ica
ti
o
n
s,
C
o
n
tr
o
l
a
n
d
S
y
ste
ms
En
g
in
e
e
rin
g
IJ
CCCE
,
v
o
l.
1
2
,
n
o
.
2
,
p
p
.
4
7
-
5
6
,
2
0
1
2
.
[
8
]
M
.
Ora
v
e
c
,
e
t
a
l
.
,
“
M
o
b
il
e
e
a
r
re
c
o
g
n
it
io
n
a
p
p
li
c
a
ti
o
n
,
”
in
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
S
y
ste
ms
,
S
i
g
n
a
ls
a
n
d
Ima
g
e
Pro
c
e
ss
in
g
(IW
S
S
IP)
,
2
0
1
6
.
[
9
]
M
o
ra
les
,
e
t
a
l
.,
“
Earp
r
in
t
re
c
o
g
n
i
ti
o
n
b
a
se
d
o
n
a
n
e
n
se
m
b
le
o
f
g
l
o
b
a
l
a
n
d
lo
c
a
l
fe
a
tu
re
s,”
In
ter
n
a
ti
o
n
a
l
Ca
r
n
a
h
a
n
Co
n
fer
e
n
c
e
o
n
S
e
c
u
rity
T
e
c
h
n
o
lo
g
y
(ICCS
T
)
,
IEE
E
,
2
0
1
5
.
[
1
0
]
Om
a
ra
,
e
t
a
l
.,
“
A
n
o
v
e
l
g
e
o
m
e
tri
c
fe
a
tu
re
e
x
trac
ti
o
n
m
e
th
o
d
fo
r
e
a
r
re
c
o
g
n
it
io
n
,
”
El
se
v
ier,
Ex
p
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
6
5
,
p
p
.
1
2
7
-
1
3
5
,
2
0
1
6
.
[
1
1
]
G
.
M
a
wlo
u
d
,
e
t
a
l
,
“
S
p
a
rse
c
o
d
i
n
g
j
o
in
t
d
e
c
isio
n
ru
le
fo
r
e
a
r
p
r
in
t
r
e
c
o
g
n
it
i
o
n
,
”
Op
t
ica
l
En
g
in
e
e
rin
g
,
v
o
l.
5
5
,
n
o
.
9
,
2
0
1
6
[
1
2
]
F
.
I.
Ey
io
k
u
r,
D
.
Ya
m
a
n
a
n
d
H.
K.
Ek
e
n
e
l,
"
D
o
m
a
in
a
d
a
p
tati
o
n
f
o
r
e
a
r
re
c
o
g
n
it
i
o
n
u
sin
g
d
e
e
p
c
o
n
v
o
l
u
ti
o
n
a
l
n
e
u
ra
l
n
e
two
rk
s,
"
i
n
IET
Bi
o
me
trics
,
v
o
l
.
7
,
n
o
.
3
,
p
p
.
1
9
9
-
2
0
6
,
2
0
1
8
.
[
1
3
]
A.
S
e
p
a
s
-
M
o
g
h
a
d
d
a
m
,
F
.
P
e
re
ira
a
n
d
P
.
L.
C
o
rre
ia,
"
Ear
re
c
o
g
n
it
io
n
i
n
a
li
g
h
t
f
ield
ima
g
in
g
fra
m
e
wo
rk
:
a
n
e
w
p
e
rsp
e
c
ti
v
e
,
"
in
IE
T
Bi
o
me
trics
,
v
o
l.
7
,
n
o
.
3
,
p
p
.
2
2
4
-
2
3
1
,
2
0
1
8
.
[
1
4
]
A.M
.
Ku
m
a
r,
e
t
a
l
.
,
“
Lo
c
a
l
Bi
n
a
ry
P
a
tt
e
rn
b
a
se
d
M
u
lt
imo
d
a
l
B
io
m
e
tri
c
Re
c
o
g
n
i
ti
o
n
u
si
n
g
Ear
a
n
d
F
KP
with
F
e
a
tu
re
Lev
e
l
F
u
si
o
n
,
”
IEE
E
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fe
re
n
c
e
o
n
In
tell
ig
e
n
t
T
e
c
h
n
i
q
u
e
s
in
Co
n
tro
l
,
O
p
ti
miza
ti
o
n
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
(INCOS
)
,
I
EE
E,
2
0
1
9
.
[
1
5
]
N.
Ku
m
a
r,
“
A
No
v
e
l
Th
re
e
P
h
a
se
Ap
p
ro
a
c
h
fo
r
S
in
g
le
S
a
m
p
le
Ear
Re
c
o
g
n
it
io
n
,
”
In
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
t
in
g
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
S
p
ri
n
g
e
r,
C
h
a
m
,
2
0
1
9
.
[
1
6
]
S
imo
-
S
e
rra
,
e
t
a
l
.
,
"
Lea
rn
i
n
g
to
s
imp
li
fy
:
fu
ll
y
c
o
n
v
o
lu
t
io
n
a
l
n
e
two
rk
s
f
o
r
ro
u
g
h
s
k
e
tch
c
lea
n
u
p
,
"
ACM
T
ra
n
s.
o
n
Gr
a
p
h
ics
(T
OG
),
v
o
l.
4
,
n
o
.
3
5
,
p
.
p
p
.
1
2
1
,
2
0
1
6
.
[
1
7
]
S
.
Ch
ris,
e
t
a
l
.,
"
F
i
n
g
e
rp
h
o
t
o
re
c
o
g
n
i
ti
o
n
with
sm
a
rtp
h
o
n
e
c
a
m
e
ra
s,"
BIOSIG
-
P
ro
c
e
e
d
in
g
s
o
f
t
h
e
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
f
Bi
o
me
trics
S
p
e
c
ia
l
In
ter
e
st Gro
u
p
(BI
OS
IG)
,
IEE
E
,
2
0
1
2
.
[
1
8
]
Wu
,
e
t
a
l
.,
"
In
tr
o
d
u
c
ti
o
n
t
o
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
rk
s,
"
Na
t
io
n
a
l
Ke
y
L
a
b
f
o
r
No
v
e
l
S
o
ft
w.
T
e
c
h
n
o
l
.
,
Na
n
ji
n
g
Un
ive
rs
it
y
,
2
0
1
7
.
[
1
9
]
S
tu
tz,
e
t
a
l
.
,
"
Ne
u
ra
l
C
o
d
e
s f
o
r
I
m
a
g
e
Re
tri
e
v
a
l,
"
Ne
u
ra
l
Co
d
e
s fo
r Ima
g
e
Retrie
v
a
l,
p
p
.
5
8
4
-
5
9
9
,
2
0
1
4
.
[
2
0
]
Ke
v
in
Ja
rre
tt
,
e
t
a
l
.
,
“
Wh
a
t
is
t
h
e
b
e
st
m
u
lt
i
-
sta
g
e
a
rc
h
it
e
c
tu
re
fo
r
o
b
jec
t
re
c
o
g
n
it
io
n
?
”
In
Co
mp
u
ter
Vi
sio
n
,
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
,
K
y
o
to
,
Ja
p
a
n
,
S
e
p
tem
b
e
r
2
0
0
9
,
p
p
.
2
1
4
6
-
2
1
5
3
.
[
2
1
]
D.
S
tu
tz,
“
Ne
u
ra
l
c
o
d
e
s f
o
r
ima
g
e
re
tri
e
v
a
l,
”
Pro
c
e
e
d
in
g
o
f
t
h
e
Co
mp
u
ter
Vi
si
o
n
-
ECCV
,
Z
u
rich
,
S
w
it
z
e
rlan
d
,
2
0
1
4
.
[
2
2
]
“
IIT
De
lh
i
T
o
u
c
h
les
s
P
a
lmp
rin
t
Da
tab
a
se
v
e
rsio
n
1
.
0
”
[O
n
l
in
e
].
Av
a
il
a
b
le:
h
tt
p
:
//
we
b
o
l
d
.
i
it
d
.
a
c
.
i
n
/~
b
i
o
m
e
tri
c
s/Da
tab
a
se
_
Ear.h
tm
l
[
2
3
]
Ku
m
a
r,
e
t
a
l
.,
“
Au
to
m
a
ted
Ear
I
d
e
n
ti
fica
ti
o
n
u
sin
g
Ear
Im
a
g
in
g
,
”
Pa
tt
e
rn
Rec
o
g
n
it
io
n
,
v
o
l.
4
5
,
n
o
.
3
,
p
p
.
9
5
6
-
9
6
8
,
2
0
1
2
.
[
2
4
]
G
e
o
rg
e
,
e
t
a
l
.,
“
Re
a
l
-
ti
m
e
e
y
e
g
a
z
e
d
irec
ti
o
n
c
las
sifica
ti
o
n
u
si
n
g
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
,
”
I
n
:
In
ter
n
a
ti
o
n
a
l
c
o
n
fer
e
n
c
e
o
n
si
g
n
a
l
p
ro
c
e
ss
in
g
a
n
d
c
o
mm
u
n
ica
ti
o
n
s (
S
PCOM
)
,
2
0
1
6
.
[
2
5
]
R.
R.
Om
a
r,
T.
Ha
n
,
S
.
A.
M
.
Al
-
S
u
m
a
id
a
e
e
a
n
d
T.
Ch
e
n
,
"
De
e
p
fin
g
e
r
tex
t
u
re
lea
rn
in
g
fo
r
v
e
rif
y
i
n
g
p
e
o
p
le,
"
i
n
IET
Bi
o
me
trics
,
v
o
l
.
8
,
n
o
.
1
,
p
p
.
4
0
-
4
8
,
1
2
0
1
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.