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Overview of Fingerprint Recognition
Advantages and Disadvantages of Fingerprint
Recognition
| ADVANTAGES |
DISADVANTAGES |
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Since fingerprints are the composition of protruding sweat glands,
everyone has unique fingerprints. |
Vulnerable to noise and distortion brought on by dirt and twists.
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| They
do not change naturally. |
Some
people may feel offended about placing their fingers on the same
place where many other people have continuously touched. |
|
Its reliability and stability is higher compared to the iris, voice,
and face recognition method. |
Some
people have damaged or eliminated fingerprints. |
| Fingerprint
recognition equipment is relatively low-priced compared to other
biometric system and R&D investments are very robust
in this field. |
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Fingerprint Sensor
There are two main types of sensors for inputting
fingerprints. One is an optical sensor using a prism or hologram, and
the other type is a non-optical sensor. Recently, products employing
both optical and non-optical methods have been introduced. In the past,
a semiconductor sensor was the only non-optical choice, but now equipment
with ultrasonic sensors, another type of non-optical sensors, are on
the market.
Optical Fingerprint Sensor
Absoprtion
Figure 5.1 explains the basic principle of
absorption in an optical fingerprint sensor.
* Figure 5.1 Principle of Absorption in Fingerprint
Sensor
An absorption optical fingerprint sensor is composed
of a right-angled triangle prism (4), light source (20), a diffusion
plate (3), a lens group and an image sensor (6).
When a fingerprint is placed on the contact surface, its ridges are
closely pressed onto the surface while its valleys are detached from
it.
The light radiated from light source becomes uniform after undergoing
the diffusion plate. The light reaches the fingerprint contact surface
after passing through the prism. If the light touches the valley, total
internal reflection happens so that it reaches the image sensor composed
of CCD (Carge Coupled Device) element or CMOS (Complementary Metal Oxide
Semiconductor) element after going through the lens group. On the other
hand, if the light reaches the ridges closely pushed onto the surface,
some light goes to the image sensor after the total internal reflection
and some light is absorbed in the ridges.
There are changes in luminous intensity between light reflected from
valleys and light from ridges and the image sensor obtains the fingerprint
image by calculating the changes in the reflected light intensity between
the two. The absorption optical fingerprint sensor needs several LEDs
(15-20) since the light should be two-dimensionally uniform after going
through the diffusion plate. To capture a fingerprint image without
distortion brought on by different optical paths, enough distance is
required between the prism and the image sensor.
Scattering
The scattering optical
fingerprint sensor is mainly comprised of a rectangular-triangle prism
(13), light source (20), a lens group (15), and an image sensor (16).
When a fingerprint is placed on the contact surface, its ridges are
closely pressed onto the surface while its valleys are detached from
it.
The light radiated from the source passes through the prism and reaches
the surface. The light perpendicularly goes through the surface unlike
the absorption sensor. If the light reaches the valleys, it goes through
the surface, radiating to the outside. If it touches upon the ridges,
scattering happens at the ridges. The scattered light gets to the image
sensor composed of CCD or CMOS element through the lens group. The light
radiated to outside near the valleys seldom reaches the image sensor.
Only the scattered light near the ridges gets to the sensor. As a result,
a fingerprint image can be captured since the valley area is dark and
the ridge area is bright.
The scattering optical fingerprint sensor doesn't need a diffusion plate
and its contrast is great. However, it needs the rectangular prism,
more expensive than the triangle prism.
* Figure 5.2 Principle of scattering fingerprint
sensor
Semiconductor Fingerprint Sensor
A semiconductor fingerprint sensor is a
prime example of non-optical sensors. Figure 5.3 shown below describes
the basic principle of the semiconductor fingerprint sensor.
* Figure 5.3 Basic principle of the semiconductor
fingerprint sensor
The semiconductor fingerprint sensor measures
the electrostatic capacity between sensor surface and skin, and translates
it into an image. If a user places his or her fingerprint on the surface,
its ridges are closely pressed on the surface and its valleys have
some space from the surface. In the case of the ridges, the distance
(d) between ridges and surface is short so that the electrostatic
capacity is high. On the other hand, the valleys are distant from
the surface compared to the ridges, so the electrostatic capacity
is low.
The fingerprint image can be captured by composing signals obtained
from an array of sensors on the semiconductor surface. The semiconductor
fingerprint sensor can be lighter and smaller. But it is vulnerable
to external shocks and chemical substances such as sodium chloride
from people's skin due to the physical traits of a silicon wafer,
which is fundamental to the sensor. To address these disadvantages,
the contact surface is being coated. Developing a physically strong
coating is one of the major tasks facing semiconductor fingerprint
sensors.
| Classification |
Optical |
Non-optical |
| Recognition
means |
Light
|
Pressure,
heat, contact, static electricity, ultrasound |
| Advantages |
Very safe.
High perception rates. Strong against external shocks and scratch. |
Impossible
to duplicate. Able to minimize the size. Low production and maintenance
costs. |
| Disadvantages |
Relatively
big module. High production and maintenance costs. |
Sensitive
to environmental changes such as static electricity and temperatures. |
* Table 5.1 Comparison between optical and non-optical methods
Fingerprint Minutiae
As shown in Figure 5.4 below, the flows of the black lines are called
ridges. Space between the nearing ridges is called a valley. The flows
of the ridges that continue or are divided constitute a particular finger-
print. An ending point is the point at which a ridge ends, and a bifurcation
point is the point at which a ridge is divided into two ridges. These
points are called minutiae and are really important information for
the classification of an automatic fingerprinting system. There are
also other important points for bulk fingerprinting DB:a core point
at which the highest or lowest ridge is shown and a delta where three
ridges from three different directions converge. Figure 5.4 shown below
displays the minutiae on an actual fingerprint image.
* Figure 5.4 Minutiae in the actual fingerprint
image
Fingerprint Recognition Algorithm
To verify the identity of a user by automatically
extracting minutiae from his or her fingerprint image, a fingerprint
recognition algorithm is required. The fingerprint recognition algorithm
is composed of two main technologies: image processing technology that
captures the characteristics of the corresponding fingerprint by having
the image under- going several stages, and matching algorithm technology
that authenticates the identity by comparing feature data comprised
of minutiae with Templates in a database. Figure 5.5 shown below explains
the overall block map of the fingerprint recognition algorithm consisting
of the two technologies.
* Figure 5.5 Block map of the fingerprint recognition
algorithm consisting of the two technologies
Image Processing
This part consists of six stages. At the image enhancement stage, noise
on the input fingerprint image is eliminated and contrast is fortified
for the sake of successive stages. At the image analysis stage, area
where fingerprint is severely corrupted is cut out to prevent adverse
effects on recognition. The binarization stage is designed to binarize
a gray-level fingerprint image. The thinning stage thins the binarized
image. The ridge reconstruction stage reconstructs the ridges by removing
pseudo minutiae. At the last stage, minutiae are extracted from the
reconstructed ridge image.
Matching
After obtaining feature data of a specific
fingerprint, compare the corresponding user who is already stored in
the DB with Templates. If the fingerprint is immensely destructed and
only general ridges, not minutiae, can be recognized, two algorithms
can be used in parallel: an algorithm based on minutiae and an algorithm
based on the overall ridge shape.
Matching stages show big differences according
to their types although they are based on the same minutiae. Here, the
most well-known matching algorithm will be briefly explained. The matching
process consists of four main stages. First of all, the minutiae analysis
stage analyzes the geometric characteristics such as distance and angle
between standard minutiae and its neighboring minutiae based on the
analysis of the image-processed feature data. After the analysis, all
the minutiae pairs have some kind of geometric relationship with their
neighboring minutiae, and the relationship will be used as basic information
for local similarity measurement.
In Figure 5.7, picture (a) shows feature data of
the input fingerprint, and (b) shows the already stored Template. Finding
a similar minutiae pair in (b) against a minutiae pair in (a) is the
local similarity measurement.
Global similarity measurement means calculating
similarity of two fingerprints by finding minutiae pairs in the local
similarity measurement in both feature data and selecting the greatest
matching minutiae pairs in the feature data. Lastly, calculating final
matching scores with the global similarity value and comparing them
with the previously set critical value verifies the identity of the
user.
Figure 1.1 Fingerprint Enrollment and Authentication
Processes in a General Fingerprint Recognition System
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