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Re
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in
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v
e
h
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u
sin
g
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e
x
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n
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t
m
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u
rp
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u
sin
g
th
e
f
e
a
tu
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n
ts.
T
h
is
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h
n
iq
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e
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a
p
p
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d
t
o
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ll
t
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c
k
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p
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g
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ti
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n
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u
ra
l
Ne
t
w
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rk
(BP
NN
).
T
h
e
e
v
a
lu
a
ti
o
n
o
f
th
e
s
y
ste
m
is
b
a
se
d
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n
p
u
b
li
c
d
a
t
a
b
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se
.
K
ey
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r
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s
:
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.
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rig
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C
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p
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:
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n
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6
0
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1
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2
,
C
h
e
n
n
ai,
I
n
d
ia.
1.
I
NT
RO
D
UCT
I
O
N
Au
to
m
a
tic
n
u
m
b
er
p
late
r
ec
o
g
n
itio
n
ca
n
b
e
u
s
ed
to
s
to
r
e
th
e
i
m
a
g
es
ca
p
tu
r
ed
b
y
t
h
e
ca
m
er
as
as
w
ell
as
th
e
tex
t
f
r
o
m
th
e
licen
s
e
p
late,
w
it
h
s
o
m
e
co
n
f
i
g
u
r
ab
le
to
s
to
r
e
a
p
h
o
to
g
r
ap
h
o
f
t
h
e
d
r
iv
er
.
S
y
s
te
m
s
co
m
m
o
n
l
y
u
s
e
i
n
f
r
ar
ed
lig
h
ti
n
g
to
allo
w
t
h
e
ca
m
er
a
to
t
ak
e
th
e
p
ict
u
r
e
at
an
y
ti
m
e
o
f
th
e
d
a
y
.
A
NP
R
tech
n
o
lo
g
y
m
u
s
t ta
k
e
i
n
to
ac
co
u
n
t p
late
v
ar
ia
tio
n
s
f
r
o
m
p
lac
e
to
p
lace
.
1
.
1
B
a
ck
g
ro
un
d
R
ev
ie
w
b
ase
d
o
n
lice
n
s
e
p
lat
e
r
ec
o
g
n
it
io
n
s
y
s
te
m
al
g
o
r
ith
m
s
is
ex
p
lai
n
ed
i
n
[
1
]
.
R
esea
r
ch
ab
o
u
t
L
P
R
i
s
i
n
v
e
s
ti
g
ated
in
th
is
ex
is
t
in
g
m
et
h
o
d
b
y
an
al
y
zi
n
g
t
h
e
f
ea
tu
r
e
s
p
r
o
s
/co
n
s
a
n
d
its
al
g
o
r
ith
m
s
ar
e
co
m
p
ar
ed
w
it
h
it
s
p
er
f
o
r
m
a
n
c
e.
E
d
g
e
d
etec
tio
n
b
ased
lice
n
s
e
p
late
au
to
m
atic
r
ec
o
g
n
itio
n
is
d
escr
ib
ed
in
[
2
]
.
L
ice
n
s
e
p
late
is
id
en
ti
f
ied
u
s
i
n
g
au
to
m
atic
licen
s
e
p
late
r
ec
o
g
n
itio
n
s
y
s
te
m
a
n
d
it
h
as
f
o
u
r
s
tep
s
t
h
at
i
s
n
o
is
e
r
e
m
o
v
al
u
s
in
g
FMH
f
ilter
,
b
ac
k
g
r
o
u
n
d
s
u
b
tr
ac
tio
n
u
s
i
n
g
s
o
m
e
al
g
o
r
it
h
m
,
p
late
lo
ca
lizatio
n
u
s
i
n
g
ca
n
n
y
ed
g
e
d
etec
tio
n
an
d
ex
tr
ac
tio
n
o
f
d
ig
its
an
d
letter
s
u
s
i
n
g
te
m
p
la
te
m
atc
h
in
g
tec
h
n
iq
u
e.
T
em
p
late
m
atch
in
g
a
n
d
m
o
r
p
h
o
lo
g
ical
ap
p
r
o
ac
h
b
ased
licen
s
e
p
late
d
etec
tio
n
a
n
d
ch
ar
ac
ter
r
ec
o
g
n
itio
n
s
y
s
te
m
f
o
r
co
m
m
er
cial
v
e
h
icle
s
is
p
r
ese
n
ted
in
[
3
]
.
L
icen
s
e
p
late
is
tr
a
ck
ed
u
s
i
n
g
co
n
to
u
r
alg
o
r
ith
m
in
t
h
e
v
eh
icle.
Ho
r
izo
n
tal
p
r
o
j
ec
tio
n
w
it
h
t
h
e
h
elp
o
f
t
h
r
es
h
o
ld
is
u
s
ed
to
s
ep
ar
ate
r
eg
is
tr
atio
n
in
f
o
r
m
atio
n
.
Ver
tica
l
p
r
o
j
ec
ti
o
n
tech
n
iq
u
e
i
s
u
s
ed
to
s
eg
m
e
n
t
th
e
d
i
g
it
s
an
d
ch
ar
ac
ter
s
w
i
th
th
r
e
s
h
o
ld
v
al
u
e.
A
t
la
s
t
te
m
p
lat
e
m
a
tch
i
n
g
is
u
s
ed
to
r
ec
o
g
n
ize
th
e
c
h
ar
ac
ter
.
E
x
tr
ac
ted
f
ea
tu
r
e
s
b
ased
r
ec
o
g
n
itio
n
o
f
lice
n
s
e
p
late
is
d
escr
ib
ed
in
[
4
]
.
No
r
m
aliza
tio
n
tech
n
iq
u
e
is
u
s
e
d
to
r
em
o
v
e
u
n
w
a
n
ted
ed
g
e
s
an
d
v
er
tica
l
ed
g
e
d
etec
tio
n
al
g
o
r
ith
m
i
s
u
s
ed
.
Mo
r
p
h
o
lo
g
ical
an
d
s
tati
s
tical
tech
n
iq
u
e
s
ar
e
u
s
ed
to
ex
tr
ac
t
th
e
L
P
r
eg
io
n
.
Fin
all
y
,
te
m
p
late
m
a
tch
i
n
g
is
u
s
ed
f
o
r
ch
ar
ac
ter
r
ec
o
g
n
i
ti
o
n
.
B
P
NN
b
ased
licen
s
e
p
late
s
eg
m
en
tatio
n
a
n
d
r
ec
o
g
n
itio
n
o
f
C
h
i
n
ese
v
e
h
icl
e
is
d
i
s
cu
s
s
ed
i
n
[
5
]
.
Ver
tical
p
r
o
j
ec
tio
n
m
et
h
o
d
is
u
s
ed
t
o
s
eg
m
e
n
t
t
h
e
L
P
.
B
P
NN
tech
n
iq
u
e
is
u
s
ed
f
o
r
r
ec
o
g
n
itio
n
u
s
i
n
g
g
e
n
er
ated
t
r
ain
in
g
m
o
d
el.
R
e
s
ea
r
ch
b
a
s
e
d
o
n
lice
n
s
e
p
late
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Licen
s
e
P
la
te
R
ec
o
g
n
itio
n
Usi
n
g
Un
d
ec
ima
ted
W
a
ve
let
Tr
a
n
s
fo
r
m
(
G.
K
a
n
n
a
n
)
559
r
ec
o
g
n
itio
n
al
g
o
r
ith
m
i
s
ex
p
la
in
ed
in
[
6
]
.
Dif
f
er
en
tia
l
i
n
teg
r
al
p
r
o
j
ec
tio
n
is
u
s
ed
i
n
t
h
is
m
eth
o
d
to
s
o
lv
e
s
o
m
e
ex
is
ted
p
r
o
b
le
m
.
O
n
e
o
f
th
e
L
P
R
alg
o
r
ith
m
s
is
in
tr
o
d
u
ce
d
i
n
th
i
s
m
eth
o
d
to
r
ec
o
g
n
ize
t
h
e
p
late.
Me
d
ia
A
cc
es
s
Dela
y
a
n
d
T
h
r
o
u
g
h
p
u
t
An
al
y
s
is
o
f
Vo
ice
C
o
d
ec
w
i
th
Si
le
n
ce
S
u
p
p
r
ess
io
n
o
n
W
ir
eless
A
d
Ho
c
Net
w
o
r
k
is
d
is
cu
s
s
ed
in
[
7
]
.
T
h
e
p
a
p
er
[
8
]
p
r
esen
ts
cr
y
p
to
g
r
ap
h
y
al
g
o
r
ith
m
w
it
h
f
o
r
ef
f
ec
ti
v
e
d
ata
co
m
m
u
n
icatio
n
.
W
av
elets
f
o
r
s
p
ea
k
er
r
ec
o
g
n
it
io
n
u
s
in
g
GM
M
clas
s
if
ier
is
d
is
cu
s
s
ed
i
n
[
9
]
.
Ob
j
ec
t
r
ec
o
g
n
itio
n
b
ased
o
n
L
B
P
an
d
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
is
d
is
c
u
s
s
ed
i
n
[
1
0
]
.
Sig
n
lan
g
u
a
g
e
r
ec
o
g
n
it
io
n
s
y
s
te
m
s
i
m
u
lated
f
o
r
v
id
eo
ca
p
tu
r
ed
w
it
h
s
m
ar
t
p
h
o
n
e
f
r
o
n
t
ca
m
er
a
is
d
is
c
u
s
s
ed
in
[
1
1
]
.
Sp
ee
ch
r
ec
o
g
n
itio
n
u
s
i
n
g
co
m
b
i
n
ed
f
u
zz
y
a
n
d
an
t
co
lo
n
y
alg
o
r
it
h
m
is
d
i
s
cu
s
s
ed
in
[
1
2
]
.
Face
r
ec
o
g
n
itio
n
u
s
i
n
g
m
u
lt
i
r
eg
io
n
p
r
o
m
in
e
n
t
L
B
P
r
ep
r
esen
tatio
n
is
d
is
cu
s
s
ed
i
n
[
1
3
]
.
1
.
2
T
he
P
ro
ble
m
Du
r
in
g
t
h
e
licen
ce
p
late
r
ec
o
g
n
i
tio
n
b
y
m
ea
n
s
o
f
t
h
e
i
m
ag
e
p
r
o
ce
s
s
in
g
tec
h
n
iq
u
e
t
h
er
e
m
a
y
s
o
m
e
m
is
-
r
ec
o
g
n
itio
n
o
cc
u
r
r
ed
d
u
r
in
g
t
h
e
r
ec
o
g
n
itio
n
p
r
o
ce
s
s
is
d
o
n
e
b
y
m
ea
n
s
o
f
a
n
y
e
x
ter
n
a
l
s
u
b
s
ta
n
ce
t
h
at
m
a
y
af
f
ec
t
t
h
e
v
is
io
n
o
f
t
h
e
ca
m
er
a
o
r
o
n
t
h
e
n
u
m
b
er
p
lates
th
at
ar
e
u
s
ed
f
o
r
r
ec
o
g
n
itio
n
.
D
u
e
to
th
i
s
t
h
e
r
ec
o
g
n
itio
n
m
a
y
also
b
e
af
f
ec
t
ed
.
So
to
i
m
p
r
o
v
e
t
h
e
r
ec
o
g
n
it
io
n
r
ate
w
e
ar
e
i
m
p
le
m
en
t
in
g
a
n
e
w
lice
n
ce
p
lat
e
r
ec
o
g
n
itio
n
m
et
h
o
d
.
1
.
3
P
r
o
po
s
ed
So
lutio
ns
Veh
icle
p
late
r
ec
o
g
n
itio
n
i
s
th
e
ai
m
o
f
t
h
is
p
r
o
p
o
s
ed
s
y
s
te
m
in
an
i
m
ag
e
e
f
f
icie
n
tl
y
.
P
u
b
lic
d
atab
ase
is
u
s
ed
to
r
ec
o
g
n
ize
t
h
e
p
late
b
ased
o
n
UW
T
an
d
B
P
NN.
Featu
r
e
ex
tr
ac
tio
n
a
n
d
clas
s
i
f
icat
io
n
is
th
e
t
w
o
m
ai
n
b
lo
ck
s
i
n
t
h
e
m
e
th
o
d
o
lo
g
y
.
I
t
is
co
m
p
o
s
ed
o
f
t
w
o
co
m
p
u
ta
tio
n
al
b
lo
ck
s
;
f
ea
t
u
r
e
o
r
i
n
f
o
r
m
atio
n
e
x
tr
ac
tio
n
,
an
d
class
i
f
icat
io
n
.
B
lo
ck
d
iag
r
a
m
o
f
t
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
o
f
L
P
R
is
s
h
o
w
n
in
t
h
e
b
elo
w
f
i
g
u
r
e.
Fig
u
r
e
1
.
P
r
o
p
o
s
ed
B
lo
ck
Diag
r
a
m
o
f
L
P
R
No
d
ec
i
m
atio
n
is
al
s
o
ca
lled
as
u
n
d
ec
i
m
ated
.
UW
T
is
ap
p
lied
o
n
b
o
th
u
p
s
a
m
p
l
in
g
i
n
t
h
e
r
ev
er
s
e
w
a
v
elet
tr
an
s
f
o
r
m
an
d
d
o
w
n
s
a
m
p
li
n
g
i
n
t
h
e
f
o
r
w
ar
d
w
av
elet
tr
an
s
f
o
r
m
p
r
o
ce
s
s
.
B
e
tter
p
ea
k
d
etec
tio
n
ca
p
ab
ilit
y
,
tr
an
s
la
tio
n
i
n
v
ar
ia
n
ce
,
ca
p
ab
ilit
y
a
n
d
b
etter
d
en
o
is
in
g
ar
e
th
e
f
ea
t
u
r
es o
f
t
h
e
U
W
T
.
2.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
I
n
t
h
is
s
ec
tio
n
,
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
te
m
b
ased
o
n
t
h
e
u
n
d
ec
i
m
ated
f
ea
t
u
r
es
i
s
d
is
cu
s
s
ed
an
d
v
er
i
f
ied
.
Feat
u
r
es
f
r
o
m
UW
T
ar
e
g
i
v
en
as
in
p
u
t
to
t
h
e
cla
s
s
i
f
icat
io
n
s
tag
e.
B
P
NN
is
u
s
ed
a
s
class
i
f
ier
to
class
i
f
y
t
h
e
i
m
ag
e
s
f
r
o
m
t
h
e
tr
ain
ed
i
m
a
g
es
to
r
ec
o
g
n
ize
t
h
e
v
e
h
icle.
T
h
e
class
if
ica
tio
n
ac
cu
r
ac
y
is
u
s
ed
to
an
al
y
ze
t
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
s
y
s
te
m
.
Fi
g
u
r
e
2
s
h
o
w
s
t
h
e
s
a
m
p
le
o
b
j
ec
ts
in
th
e
p
u
b
lic
d
atab
ase.
Fig
u
r
e
2
.
Sa
m
p
le
Veh
icle
P
late
Dete
ctio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
9
,
No
.
3
,
Ma
r
ch
2
0
1
8
:
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5
8
–
5
6
0
560
3.
CO
NCLU
SI
O
N
I
n
th
i
s
p
ap
er
,
an
ap
p
r
o
ac
h
f
o
r
L
P
R
b
ased
o
n
UW
T
f
ea
tu
r
es
is
p
r
esen
ted
.
Un
d
ec
i
m
ated
c
o
ef
f
icie
n
t
s
ar
e
u
s
ed
f
o
r
f
ea
t
u
r
e
ex
tr
ac
tio
n
.
Ob
tain
ed
f
ea
t
u
r
es
ar
e
ap
p
lied
to
th
e
class
i
f
icatio
n
s
ta
g
e.
B
P
NN
class
i
f
ier
u
s
e
s
th
e
tr
ai
n
ed
i
m
a
g
es
f
r
o
m
t
h
e
f
ea
t
u
r
e
e
x
tr
ac
tio
n
s
ta
g
e
a
s
r
ef
er
e
n
ce
s
an
d
clas
s
if
ies
th
e
test
o
b
j
ec
ts
.
T
h
e
class
i
f
icatio
n
p
er
f
o
r
m
a
n
ce
o
f
th
e
UW
T
b
ased
s
y
s
te
m
i
s
e
v
a
lu
ated
b
y
u
s
i
n
g
clas
s
i
f
icatio
n
r
ate
in
p
er
ce
n
tag
e.
E
x
p
er
i
m
e
n
tal
r
es
u
lts
s
h
o
w
t
h
a
t th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
p
r
o
d
u
ce
s
9
8
% a
cc
u
r
ac
y
.
RE
F
E
R
E
NC
E
S
[1
]
Ba
k
h
tan
M
A
H,
A
b
d
u
ll
a
h
M
,
Ra
h
m
a
n
AA
.
A
re
v
iew
o
n
L
ice
n
se
Pl
a
te
Rec
o
g
n
it
i
o
n
sy
ste
m
a
lg
o
rit
h
ms
,
IEE
E
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
In
f
o
r
m
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
.
2
0
1
6
;
8
4
-
8
9
.
[2
]
Ha
P
S
,
S
h
a
k
e
ri
M
.
L
ice
n
se
P
late
A
u
to
m
a
ti
c
Re
c
o
g
n
it
io
n
b
a
se
d
o
n
e
d
g
e
d
e
tec
ti
o
n
,
IEE
E
Arti
fi
c
i
a
l
In
telli
g
e
n
c
e
a
n
d
Ro
b
o
ti
c
s.
2
0
1
6
;
1
7
0
-
1
7
4
.
[3
]
Ro
y
A
C,
Ho
ss
e
n
M
K,
Na
g
D.
L
ice
n
se
p
la
te
d
e
tec
ti
o
n
a
n
d
c
h
a
ra
c
ter
re
c
o
g
n
it
io
n
sy
ste
m
fo
r
c
o
m
me
rc
ia
l
v
e
h
icle
s
b
a
se
d
o
n
mo
rp
h
o
l
o
g
ic
a
l
a
p
p
ro
a
c
h
a
n
d
tem
p
la
te
ma
tch
in
g
,
IE
EE
3
rd
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
o
n
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
lo
g
y
.
2
0
1
6
;
1
-
6.
[4
]
S
a
lee
m
N,
M
u
a
z
z
a
m
H,
T
a
h
ir
HM,
F
a
ro
o
q
U.
Au
t
o
ma
ti
c
li
c
e
n
se
p
la
te
re
c
o
g
n
it
io
n
u
si
n
g
e
x
tra
c
ted
fea
tu
re
s
,
IEE
E4
th
In
tern
a
ti
o
n
a
l
S
y
m
p
o
siu
m
o
n
C
o
m
p
u
tatio
n
a
l
a
n
d
Bu
si
n
e
ss
In
telli
g
e
n
c
e
.
2
0
1
6
;
2
2
1
-
2
2
5
.
[5
]
W
a
n
g
N,
Zh
u
X
,
Zh
a
n
g
J.
L
ice
n
se
Pl
a
te
S
e
g
me
n
ta
t
io
n
a
n
d
Rec
o
g
n
i
ti
o
n
o
f
Ch
i
n
e
se
Veh
icle
B
a
se
d
o
n
BP
NN
,
IEE
E1
2
th
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
C
o
m
p
u
tatio
n
a
l
In
telli
g
e
n
c
e
a
n
d
S
e
c
u
rit
y
.
2
0
1
6
;
4
0
3
-
4
0
6
.
[6
]
Yo
n
g
p
in
g
L
,
X
iao
b
o
G
.
Res
e
a
rc
h
o
n
No
v
e
l
L
ice
n
se
Pl
a
te
Re
c
o
g
n
i
ti
o
n
Al
g
o
rith
m
,
IEE
E
S
ix
t
h
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
In
telli
g
e
n
t
S
y
ste
m
s De
sig
n
a
n
d
En
g
i
n
e
e
rin
g
A
p
p
li
c
a
ti
o
n
s.
2
0
1
5
;
6
4
8
-
6
5
2
.
[7
]
S
h
a
h
RD,
S
i
n
g
h
S
K.
M
e
d
ia
Acc
e
ss
De
la
y
a
n
d
T
h
ro
u
g
h
p
u
t
A
n
a
ly
sis
o
f
Vo
ice
Co
d
e
c
wit
h
S
il
e
n
c
e
S
u
p
p
re
ss
io
n
o
n
W
ire
les
s A
d
Ho
c
Ne
two
rk
.
P
ro
c
e
d
ia Co
m
p
u
ter S
c
ien
c
e
.
2
0
1
6
;
79
;
9
4
0
-
9
4
7
.
[8
]
G
a
n
e
sh
Ku
m
a
r
K,
A
riv
a
z
h
a
g
a
n
D.
Ne
w
c
r
y
p
to
g
ra
p
h
y
a
lg
o
rit
h
m
w
it
h
f
o
r
e
ff
e
c
ti
v
e
d
a
ta
c
o
m
m
u
n
ica
ti
o
n
,
In
d
i
a
n
J
o
u
rn
a
l
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
.
2
0
1
6
;
9
(4
8
):
1
0
8
-
9
7
0
.
[9
]
V
D
KA
.
W
a
v
e
lets
f
o
r
sp
e
a
k
e
r
re
c
o
g
n
it
io
n
u
si
n
g
G
M
M
c
las
sif
ier.
In
ter
n
a
t
io
n
a
l
jo
u
rn
a
l
o
f
a
d
v
a
n
c
e
s
in
si
g
n
a
l
a
n
d
ima
g
e
sc
ien
c
e
s.
2
0
1
7
;
3
(
1
):
1
3
-
1
8
.
[1
0
]
Je
y
a
su
d
h
a
A
,
P
riy
a
K
.
Ob
jec
t
re
c
o
g
n
it
io
n
b
a
se
d
o
n
L
BP
a
n
d
d
is
c
re
te
w
a
v
e
let
tran
sf
o
r
m
.
In
ter
n
a
ti
o
n
a
l
jo
u
r
n
a
l
o
f
a
d
v
a
n
c
e
s in
si
g
n
a
l
a
n
d
ima
g
e
sc
ien
c
e
s.
2
0
1
6
;
2
(1
):
2
4
-
3
0
.
[1
1
]
Ra
o
GA
,
Kish
o
re
P
VV
.
S
ig
n
la
n
g
u
a
g
e
re
c
o
g
n
it
io
n
sy
ste
m
si
m
u
late
d
f
o
r
v
id
e
o
c
a
p
tu
re
d
w
it
h
s
m
a
rt
p
h
o
n
e
f
ro
n
t
c
a
m
e
ra
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
.
2
0
1
6
;
6
(
5
):
2
1
7
6
-
2
1
8
7
.
[1
2
]
Ja
li
li
F
,
Ba
ra
n
i
M
J.
S
p
e
e
c
h
Re
c
o
g
n
it
io
n
Us
in
g
C
o
m
b
in
e
d
F
u
z
z
y
a
n
d
A
n
t
C
o
l
o
n
y
a
lg
o
rit
h
m
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
.
2
0
1
6
;
6
(5
):
2
2
0
5
-
2
2
1
0
.
[1
3
]
Ko
n
d
a
S
R
,
Ku
m
a
r
V
,
K
rish
n
a
V
.
F
a
c
e
Re
c
o
g
n
it
io
n
u
si
n
g
M
u
lt
i
Re
g
io
n
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