![Grow R307s Cheap 1000 Capacity USB Uart Optical Fingerprint Module]()
Description
1.
Integrated
image
collecting
and
algorithm
chip
together,
ALL-in-One
2.
Fingerprint
reader
can
conduct
secondary
development,
can
be
embedded
into
a
variety
of
end
products
3.
Users
can
conduct
secondary
development,
can
be
embedded
into
a
variety
of
end
products,
such
as:
access
control,
attendance,
safety
deposit
box,
car
door
locks
4.
Low
power
consumption,
low
cost,
small
size,
excellent
performance
5.
Professional
optical
technology,
precise
module
manufacturing
technics
6.
Good
image
processing
capabilities,
can
successfully
capture
image
up
to
resolution
500
dpi
7.
Have
finger
detection
function.
8.
R307S
fingerprint
capacity:
1000
Brief
description
of
R307S
fingerprint
module:
1.
Fingerprint
capacity:
The
storage
capacity
of
R307S
fingerprint
module
is
1000.
2.
Description
of
interface:
R307-S
fingerprint
module
has
RS232
and
USB2.0
at
the
same
time.USB2.0
interface
can
connect
to
the
computer;RS232
interface
is
TTL
level,
and
the
default
baud
rate
is
57600,
which
can
be
changed.
Please
refer
to
the
communication
protocol.It
can
be
connected
with
microcontroller,
such
as
ARM,
DSP
and
other
equipment
with
serial
port,
and
the
microcontroller
of
3.3v
and
5V
can
be
connected
directly.When
connecting
to
the
computer,
please
pay
attention
to
the
level
conversion,
such
as
the
MAX232
circuit.
3.
Module
detection
After
the
fingerprint
module
is
powered
on,
the
fingerprint
collection
window
will
flash,
indicating
that
the
self-test
is
normal.
If
it
does
not
flash,
please
carefully
check
the
power
supply,
whether
it
is
connected
backwards
or
wrongly.The
chip
has
some
heat
when
working
normally,
this
is
normal
phenomenon,
the
product
has
passed
strict
test,
can
rest
assured
to
use.
Specifications
·Type:
Optical
·Backlight:
Blue
·Sensor
Life:
100
million
times
·Static
Indications:
15KV
·Character
file
Size:
256
bytes
·Template
Size:
512
bytes
·Security
Level:
5
(1,2,3,4,5(highest))
·Scanning
Speed:
<
0.3
second
·Verification
Speed:
<
0.2
second
·Matching
Method:
1:N
·FRR
(False
Rejection
Ratio):
≤0.1%
·FAR
(False
Acceptance
Ratio):
≤0.0001%
·Operating
Environment
Temperature:
-20°C
---50°C
·RS232
communications
baud
rate:
9600BPS~115200BPS
changeable
Files
·All
fingerprint
module
support
with
Arduino,
Android,
Windows,
Linux,
.Net
and
so
on.
·Provide
Free
SDK
Files
·Provide
User
Manual
Progress
in
Biological
Fingerprint
Scanning
Technology
Biometric
fingerprint
scanning
technology,
as
an
important
branch
of
biometric
recognition,
has
undergone
significant
innovation
and
progress
in
recent
years.
This
technology
is
based
on
the
uniqueness
of
individual
fingerprints
and
provides
strong
support
for
fields
such
as
identity
verification,
security
monitoring,
and
medical
identification
through
high-precision
collection
and
analysis.
In
the
early
days,
fingerprint
scanning
mainly
relied
on
traditional
ink
pressing
methods,
which
were
cumbersome
and
inefficient
to
operate.
With
the
advancement
of
technology,
the
introduction
of
advanced
acquisition
technologies
such
as
optics,
capacitance,
ultrasound,
and
biological
radio
frequency
has
greatly
improved
the
accuracy
and
convenience
of
fingerprint
recognition.
Optical
fingerprint
scanning
captures
fingerprint
images
through
the
principle
of
light
reflection,
while
capacitive
technology
utilizes
the
difference
in
capacitance
between
the
finger
and
the
sensor
to
form
fingerprint
images.
The
application
of
these
technologies
not
only
simplifies
the
collection
process,
but
also
significantly
improves
recognition
accuracy.
Ultrasonic
scanning
technology
is
a
major
highlight
in
recent
years.
It
measures
the
ultrasonic
signals
reflected
from
the
surface
of
fingerprints
to
obtain
more
precise
fingerprint
depth
information,
effectively
resisting
external
factors
such
as
dust
and
oil
interference.
Biological
radio
frequency
technology
goes
further
by
using
radio
frequency
signals
to
penetrate
the
dermis
layer
of
fingers
and
capture
internal
features
of
fingerprints,
adding
a
new
security
dimension
to
fingerprint
recognition.
At
the
algorithmic
level,
the
integration
of
advanced
technologies
such
as
machine
learning
and
deep
learning
enables
fingerprint
recognition
systems
to
process
complex
fingerprint
images
more
intelligently,
improving
recognition
speed
and
accuracy.
These
technologies,
through
continuous
learning
and
optimization,
can
more
accurately
identify
subtle
fingerprint
features
and
achieve
efficient
verification
even
under
low-quality
fingerprint
conditions.
The
application
of
fingerprint
recognition
technology
is
particularly
widespread
in
fields
such
as
financial
payments
and
security
monitoring.
In
the
field
of
smartphones,
the
emergence
of
under
screen
fingerprint
technology
not
only
increases
the
screen
to
body
ratio
of
devices,
but
also
provides
users
with
a
more
seamless
identity
verification
experience.
In
the
payment
scenario,
fingerprint
recognition
has
become
a
standard
feature
for
many
mobile
payment
platforms
and
online
banks
as
a
fast
and
secure
means
of
identity
verification.
At
the
same
time,
the
application
in
access
control
systems,
attendance
management,
and
monitoring
systems
further
strengthens
security
protection
and
management
efficiency.
However,
fingerprint
recognition
technology
also
faces
some
challenges.
Environmental
factors
such
as
dust,
sweat,
and
scars
may
affect
the
quality
of
fingerprints,
leading
to
a
decrease
in
recognition
accuracy.
In
addition,
the
threat
of
fingerprint
forgery
cannot
be
ignored.
Although
modern
technology
can
effectively
resist
most
forgery
methods,
continuous
technological
innovation
is
still
the
key
to
ensuring
security.
To
address
these
challenges,
researchers
are
working
on
developing
higher
precision
collection
technologies,
such
as
using
high-resolution
sensors
to
capture
subcutaneous
fingerprint
structure
information
and
combining
artificial
intelligence
algorithms
to
enhance
anti-counterfeiting
capabilities.
At
the
same
time,
the
integration
of
multimodal
biometric
technology,
such
as
combining
fingerprint
recognition
with
facial
recognition,
iris
recognition,
etc.,
has
also
become
an
important
direction
for
improving
security.
In
summary,
biological
fingerprint
scanning
technology
has
made
significant
progress
in
recent
years,
not
only
achieving
a
qualitative
leap
in
collection
efficiency
and
recognition
accuracy,
but
also
playing
an
irreplaceable
role
in
multiple
key
fields.
With
the
continuous
evolution
and
innovation
of
technology,
fingerprint
recognition
technology
will
continue
to
deepen
its
application
in
security
verification,
identity
management,
and
other
aspects,
contributing
to
the
construction
of
a
more
intelligent
and
secure
social
environment.
