Pattern recognition hand vascular pattern recognition

Hand Vascular Pattern Recognition (HVPR)Hand Vascular Pattern Recognition (HVPR)Hand Vascular Pattern Recognition (HVPR)Hand Vascular Pattern Recognition (HVPR)
Introduction
 The field of hand vascular pattern technology or vein
pattern technology uses the subcutaneous vascular
network on the back of the hand to verify the identity of
individuals in biometric applications. The principle of this
technology is based on the fact that the pattern of blood
vessels is unique to each individual , even between
identical twins. Therefore, the pattern of the hand blood
vessels is a highly distinctive feature that can be used for
verifying the identity of the individual. Hand vascular
pattern biometric technology is relatively new and is in
the process of being continuously refined and developed.

Introduction
 The hand vascular pattern was first considered as a
potential technology in the biometric security field in the
early 1990s. In 1992, Shimizu brought into focus the
potential for use of the hand vascular technology in his
published paper on trans-body imaging . In 1995,
Cross and Smith introduced thermographic imaging
technology for acquiring the subcutaneous vascular
network on the back of the hand for biometric
applications . Since then, a large number of research
efforts have continuously contributed to hand vascular
pattern technology. It was not until 1997 that the first
practical application was developed.

Development of Hand Vascular
Pattern Technology
 The history of development of hand vascular
pattern technology goes back to early 1997
when BK Systems announced its first
commercial product, BK-100.This product has
been mainly sold in Korean and Japanese
markets. In the early stages, the product was
limited to physical access control applications .
Fig. 13.1 shows a prototype of the BK-100 hand
vascular pattern recognition system. In 1998, the
first patent on hand vascular pattern technology
was assigned to BK systems.

Development of Hand Vascular
Pattern Technology
 This invention described and claimed an
apparatus and method for identifying individuals
through their subcutaneous vascular patterns .
Based on this invention, new commercial
versions, BK-200 and BK-300, were released to
the market. Unfortunately, the development of
these products was discontinued at the end of
1998.

Fig. 13.1. Prototype of the first hand vascular
commercial product BK-100.

Development of Hand Vascular Patten
Technology
 Fig. 13.2 shows a prototype of the VP-II product.
In order to gain wider acceptance in various
applications, VP-II was continuously improved to
adapt for large-scale identification applications.
As the number of users enrolled in the system
grew to thousands, faster processing ability and
larger storage were required. New commercial
versions, VP-II S and VP-II M , were released to
satisfy these requirements.

Fig. 13.2. VP-II Stand alone system for
personal identification.

Technology
 Hand vascular patterns are the representation of blood
vessel networks inside the back of hand.
The hand vascular pattern recognition system operates
by comparing the hand vascular pattern of a user being
authenticated against a pre-registered pattern already
stored in the database. Fig. 13.6 shows a typical
operation of the hand vascular pattern recognition
system.

Fig. 13.6. Operation of a typical vascular
biometric identification system.

Image Acquisition
 Since the hand vascular pattern lies under the skin, it
can not be seen by the human eye. Therefore, we can
not use visible light, which occupies a very narrow band
(approx. 400 - 700nm wavelength),for photographing .
Hand vascular patterns can only be captured under the
near-infrared light (approx.800 - 1000nm wavelength),
which can penetrate into the tissues.
 Blood vessels absorb more infrared radiation than the
surrounding tissue , which causes the blood vessels to
appear as black patterns in the resulting image captured
by a charge-couple device (CCD) camera.
Fig. 13.7 shows an example of hand images obtained by
visible light and near-infrared light.

Fig. 13.7. The hand image obtained by visible
light (left) and infrared light (right).
visible light infrared light

Image Acquisition
 To capture the image of blood vessels under near-infrared
light, the scanner uses an LED array to emit the light
and illuminate the hand. A CCD camera sensitive to
near-infrared light is used to photograph the image.
 A near-infrared filter attached in front of the CCD camera
is used to block all undesired visible light emitted by
external sources. The image of blood vessels can be
acquired by either reflection or transmission.

Image Acquisition
 Transmission method: The hand is illuminated by an
LED array and the CCD camera captures the light that
passes through the hand. To use this method, the LED
array is above the hand and the CCD camera is placed
on the opposite side of the LED array with respect to the
hand. Fig. 13.8 shows the configuration for the LED
array and the CCD camera.
 Reflection method: Here the hand is illuminated by an
LED array and the CCD camera captures the light that is
reflected back from the hand. So , the illumination LED
array and the CCD camera are positioned in the same
location. Fig. 13.9 shows the configuration for the
illumination LED array and the CCD camera.

Image Acquisition
 The reflection method is preferred since the transmission
method is often sensitive to changes in the hand's light
transmittance, which is easily affected by temperature
or weather. If the hand's light transmittance is relatively
high, the blood vessels are not very clear in captured
images. In contrast, the light transmittance does not
significantly affect the level or contrast of the reflected
light. Another reason why the reflection method is
preferred is due to its easy configuration. Since the
illumination LED array and the CCD camera can be
located in the same place, the system is easy to embed
into small devices.

Feature Extraction
The hand vascular images captured from the acquisition devices
contain not only the vascular patterns but also undesired noise
and irregular effects such as shadow of the hand and hairs on
the skin surface. The captured images
Fig. 13.8. Configuration of
transmission-based acquisition
method.
Fig. 13.9. Configuration of
reflection-based acquisition
method.

Feature Extraction
 should be pre-processed before being used for
verification. The aim of a feature extraction algorithm is to
accurately extract the vascular patterns from raw images.
A typical feature extraction algorithm commonly
consists of various image processing steps to remove the
noise and irregular effects, enhance the clarity of
vascular patterns, and separate the vascular patterns
from the
background.
 The final vascular patterns obtained by the feature
extraction algorithm are represented as binary images.

Fig. 13.10. The flow chart of a typical feature
extraction algorithm.
Noise
removal
algorithm
Adaptive
algorithm
Raw Image
Hand vascular extracting processing flow
Extracted Vascular Pattern
Binary image

Feature Extraction
 The noise removal algorithm is based on a low-pass filter. To improve
the clarity of vascular patterns in captured images, an enhancement
algorithm is commonly used . A number of
algorithms based on filtering techniques have been proposed
for enhancing the clarity of vascular patterns in captured images The
algorithm proposed in utilized two different preprocessing filters :
 Row Vascular Pattern Extraction Filter (RVPEF) for effective
extraction of the horizontal vascular patterns.
 Column Vascular Pattern Extraction Filter (CVPEF) for effective
extraction of the vertical vascular patterns.
The final vascular patterns are obtained by combining the outputs
from both the filters.
Fig. 13.11 shows the row chart of the direction based vascular pattern
extraction algorithm.

Fig. 13.11. Flow chart of the direction
based vascular pattern extraction algorithm

Pattern Matching
 In the matching step, the extracted vascular pattern from
the feature extraction step is compared against the
pre-registered pattern in the database to obtain
a matching score. The matching score is then used to
compare with the pre-defined system threshold value to
decide whether the user can be authenticated or not.
Typical methods that are commonly used
for pattern matching are structural matching and
template matching
 Structural matching is based on comparing locations of
feature points such as line endings and bifurcations
extracted from two patterns being compared to obtain
the matching score.

Pattern Matching
This method has been used widely in fingerprint matching.
However, unlike the fingerprint
patterns, the hand vascular patterns have fewer minutiae-
like feature points. Therefore, it is not appropriate to apply
only this method for good vascular pattern matching
results.
 Template matching is the most popular and widely used
method for matching the vascular patterns. It is based on
the comparison of pixel values of two vascular pattern
images and has been commonly used for matching
line-shaped patterns. Moreover, use of template matching
does not require any additional steps to calculate the
feature points such as line endings and bifurcations and
is robust for vascular pattern matching.

Fig. 13.12. Example of hand vascular pattern obtained by
direction-based vascular pattern extraction algorithm.

General Applications
Typical application of vascular pattern
technology can be classified as follows:
 Physical access control and Time attendance
 Finance and Banking
 Travel and Transportation
 Hospitals
 Construction Sites
 Schools

Conclusions
 Hand vascular pattern technology is relatively new, but it has already
gained considerable attention in the biometric community. This is
supported by the fact that a large number of research attempts have
been conducted to improve the technology in recent years. Since the
release of the first commercial product in 1997, thousands of units
have been installed in various applications including access control
and time and attendance, banking solutions, transportation, hospital,
construction sites, and schools.
 The rapidly increasing number of installed units in various applications
within a short time implies that hand vascular pattern technology
will be a promising technology in the security field .
Although the hand vascular pattern has provided high accuracy
and good usability, its performance may degrade under some
adverse conditions such as cold weather, undesired noise or external
sources.

Pattern recognition hand vascular pattern recognition

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