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| :: Why
Fingerprints? |
With
increasingly urgent need for reliable
security, biometrics is being spotlighted as
the authentication method for the next
generation. Among numerous biometric
technologies, fingerprint authentication has
been in use for the longest time and bears
more advantages than other biometric
technologies do.
Fingerprint authentication is possibly the
most sophisticated method of all biometric
technologies and has been thoroughly verified
through various applications. Fingerprint
authentication has particularly proved its
high efficiency and further enhanced the
technology in criminal investigation for more
than a century.
Even features such as a person’s gait, face,
or signature may change with passage of time
and may be fabricated or imitated. However, a
fingerprint is completely unique to an
individual and stayed unchanged for lifetime.
This exclusivity demonstrates that fingerprint
authentication is far more accurate and
efficient than any other methods of
authentication.
Also, a fingerprint may be taken and
digitalized by relatively compact and cheap
devices and takes only a small capacity to
store a large database of information. With
these strengths, fingerprint authentication
has long been a major part of the security
market and continues to be more competitive
than others in today’s world. |
| :: History
of Fingerprint Technology |
The
beginning of fingerprints goes back to as
early as the ancient times. According to
historical findings, fingerprints were used on
clay tablets for business transactions in
ancient Babylon. In China, thumb prints were
found on clay seals. But it was in the 19th
century that the results of scientific studies
were published and fingerprint technology
began to be considered more seriously.
Using the1800’s scientific studies as a
foundation, fingerprint technology was already
in use by the beginning of the 20th century.
In 1924, FBI(Federal Bureau of Investigation)
is already known to have maintained more than
250 million civil files of fingerprints for
the purpose of criminal investigation and the
identification of unknown casualties. In the
late 1960's, fingerprint technology met a
great turning point when it gave birth to
'live-scan,' a method to obtain a fingertip
image without the use of print ink. When the
FBI announced that it planned to stop using
paper fingerprint cards inside their new
Integrated AFIS (IAFIS) site, it was actually
announcing the remarkable breakthrough of
today's live-scan technology.
But fingerprint identification technology did
not stop as a forensic method only. It was
officially used for business purposes in 1968
at one security corporation in Wall Street.
Fingerprints are now being used as a secure
and effective authentication method in
numerous fields, including financial, medical,
e-commerce and entrance control applications.
Modern applications of fingerprint technology
rely in large part on the development of
exceptionally compact fingerprint sensors. |
Fingerprint
identification process consists of two
essential procedures: enrollment and
authentication. Taking the following steps
completes each procedure: |
 |
| As
shown in the diagram above, fingerprint
identification system compares the input
fingerprint image and previously registered
data to determine the genuineness of a
fingerprint. All the steps described above
affect the efficiency of the entire system,
but the computational load of the following
steps can be reduced to a great extent by
acquiring a good-quality fingerprint image in
the first step. |
Step
1. Image Acquisition |
| Real-time
image acquisition method is
roughly classified into
optical and non-optical.
Optical method relies on the
total reflection phenomenon on
the surface of glass or
reinforced plastic where the
fingertip is in contact. The
sensor normally consists of an
optical lens and a CCD module
or CMOS image sensor. In
contrast, semiconductor
sensors, as a typical example
of non-optical sensors,
exploit electrical
characteristics of a fingertip
such as capacitance.
Ultrasonic wave, heat, and
pressure are also utilized to
obtain images with the
non-optical fingerprint
sensors. Non-optical sensors
are said to be relatively more
suitable for massive
production and size reduction
such as in the integration
with mobile devices. Detailed
comparison is found in Table
1. |
| |
Optical |
Non-optical |
| Measuring
Method |
light |
pressure,
heat, capacitance,
ultrasonic wave |
| Strength |
highly-stable
performance
physical/electrical
durability
high-quality image |
low
cost with mass
production compact
size integrated with
low-power application |
| Weakness |
relatively
high cost
limit to
size-reduction
relatively easy to
fool with a finger
trace or fake finger |
physical/electrical
weakness
performance sensitive
to the outer
environment(temperature,
dryness of a finger) |
| Application |
entrance,
time, and attendance
control
banking service
PC security |
PC
security
e-commerce
authentication
mobile devices &
smart cards |
|
Step
2. Feature Extraction |
| There
are two main ways to compare
an input fingerprint image and
registered fingerprint data.
One is to compare an image
with another image directly.
The other is to compare the
so-called 'features' extracted
from each fingerprint image.
The latter is called
feature-based/minutia-based
matching. Every finger has a
unique pattern formed by a
flow of embossed lines called
“ridges” and hollow
regions between them called
“valleys.” As seen in the
Picture 2 below, ridges are
represented as dark lines,
while valleys are bright. |
 |
|
| Step
3. Matching |
The
matching step is classified
into 1:1 and 1:N matching
according to its purpose
and/or the number of reference
templates. 1:1 matching is
also called personal
identification or
verification. It is a
procedure in which a user
claims his/her identity by
means of an ID and proves it
with a fingerprint. The
comparison occurs only once
between the input fingerprint
image and the selected one
from the database following
the claim by the user.
On the contrary, 1:N matching
denotes a procedure where the
system determines the user's
identity by comparing the
input fingerprint with the
information in the database
without asking for the user's
claim. A good example of this
is AFIS(Automated Fingerprint
Identification System)
frequently used in criminal
investigation.
The output result of the
matching step is whether or
not the input fingerprint is
identical to the one being
compared in the database. Then
how could the accuracy of the
matching procedure be
represented in number? The
simplest measures are
FRR(False Reject Rate) and
FAR(False Accept Rate). The
former is the rate of genuine
user's rejection and the
latter is the rate of
impostor's acceptance. |
|
|
| :: Fingerprint
Application |
Markets
for fingerprint technology include entrance
control and door-lock applications,
fingerprint identification mouses, fingerprint
mobile phones, and many others. The
fingerprint markets are classified as follows: |
 |
| As
the advanced technology enables even more
compact fingerprint sensor size, the range of
application is extended to the mobile market.
Considering the growing phase of the present
mobile market, its potential is the greatest
of all application markets. |
| References |
| [1]
International Biometric Group,「Biometric
Market Report 2000-2005」,
2001. |
| [2]
Anil K. Jain, Lin Hong, Sharath Pankanti, Ruud
Bolle,「An
Identity Authentication System Using
Fingerprints」,
1997. |
| [3]
http://onin.com/fp/fphistory.html |
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