Vein rishabh

VEIN IDENTIFICATION
RESEARCH PAPER MADE BY RISHABH SHARMA, ROHIT
KUMAR, VIBHOR VARSHNEY
Email:rohitadventure9719@gmail.com,rohitkumarpatel75@gmail.co
m,omvarsh@gmail.com
Abstract:The palm vein pattern is unique biometric identity of the human beings. The palm vein recognition is a popular
biometric technique which is used for authentication purposes in various applications. In this paper a review on new novel
palm vein recognition architecture has been described. All of the techniques used to build the palm vein recognition
architecture have been discussed in this paper. A palm vein recognition system consists of the following steps: Image
acquisition from the database and Pre-Processing, Finding of Region of interest, Extraction of Palm Vein pattern
Features and Recognition. The aim of the proposed model is to improve the accuracy and response time of palm vein
authentication. The proposed model will use neural networks for the final evaluation of the testing sample and training
samples to recognize the person. The proposed model will be robust, flexible and accurate than the existing palm vein
authentication schemes.Palm vein authentication is one of the most reliable authentication. Which contain physiological
body characteristics that can be used to distinguish between users. In this paper we represent in new approach for the
personal identification in the palm vein image.This paper represent the contactless palm vein authentication device that
takes blood vessel pattern as a personal identification. It is one of the most critical and challenging task to meet upcoming
demand for stringent security. Vein are internal in the body and have wealth of differentiating feature assuming false identity
through forgery is externally difficult, thereby enabling and externally high level of security. The palm secure works by
capturing a person’s vein pattern image while radiating it with near infrared rays. The palm secure detects the structure of
the pattern of veins on palm of the human hand with the at most precision .The sensor emits a near infrared beam towards
the palm of the hand and the blood flowing through these back to the heart with reduced oxygen absorb as black pattern.
This pattern is a recorded by the sensor and is stored in encrypted from in a database, on a token or an a smart card. It is
consist of small palm vein scanner. That is easy and nature to use, fast and highly accurate.
INTRODUCTION
In the ubiquitous network society, where individuals caneasily access their information anytime and anywhere, people are
also faced with the risk that others can easily access the same information anytime and anywhere. Because of this risk,
personal identification technology is used which includesPasswords, personal identification numbers and identification
cards. However,8cards canbestolenand passwords andnumbers can be guessed or forgotten. To solve these problems, four
methods are developed: fingerprints, faces, voice prints and palm veins. Among these, because of its high accuracy, contact
less palm vein authentication technology is being incorporated into various financial solution products for use in public
places. Palm vein authentication is one of the vascular patternauthentication technologies Vascular pattern authentication
includes vein patternauthentication includes vein pattern authentication using the vein patterns of thepalm, back of the
hand or fingers as personal identification data, and retina recognition using the vascular patterns at the back of the eye
aspersonal identification.
The vascular pattern used in this authentication technology refers to the image of vessels within the body that can be seen
as a random mesh at the surface of the body. Since everyone has vessels, vascular pattern authentication can be applied to
almost allpeople. If vascular patterns were compared to the features used in other biometric authentication technologies,
such as the face, iris, fingerprint, voice, and so on, the only difference would be whether or not the feature is at the surface of
the body. Consequently, vascular patterns cannot be stolen by photo graphing, tracing, or recording them. This means that
forgery would be extremely difficultunder ordinary conditions.
An individual's palm vein image is converted by algorithms into data points, whichis then compressed, encrypted, and
stored by thesoftware and registered along with the otherdetails in his profile as a reference for future comparison.
Then, each time a person logs inattempting to gain access by a palm scan to a particular bank account or secured
entryway,etc., the newly captured image is likewise processed and compared to the registered one orto the bank of stored
files for verification, all in a period of seconds. Numbers and positions ofveins and their crossing points are all
compared and, depending on verification, the person iseither granted or denied access.

PALM VEIN TECHNOLOGY
Palm vein authentication works by comparingthe pattern of
veins in the palm(which appear as blue lines) of a person
beingauthenticated with a pattern stored in a database.Vascular
patterns are unique to each individual, according
toFujitsuresearch even identical twins have different patterns
and since the vascular patterns exist inside the body, theycannot
be stolen by means of photography,contains a wealth of
differentiating features for personal identification. The palm is
an ideal partof the body for this technology; it normally does
not have hair which can be an obstacle forphotographing the
blood vessel pattern, and it isless susceptible to a change in
skin color, unlikea finger or the back of a hand.
The deoxidized hemoglobin in the vein vessels absorbs light
having a wavelength ofabout 7.6 x 10-4 mm within the
near-infraredarea. When the infrared ray image is captured,
only the blood vessel pattern containing thedeoxidized
hemoglobin is visible as a series of dark lines. Based on this
feature, the veinauthentication device translates the black
linesof the infrared ray image as the blood vesselpattern of
the palm, and then matches it with thepreviously registered
blood vessel pattern of theindividual.
Principles of vascular pattern authentication:
Hemoglobin in the blood is oxygenated in the lungs and carries oxygen to the tissues of the body through the arteries. After
it releases its oxygen to the tissues, the deoxidizedhemoglobin returns to the heart through the veins. These two types of
hemoglobin have different rates of absorbency1. Deoxidized hemoglobin absorbs light at a wavelength ofabout 760 nm in
the near-infrared region. When the palm is illuminated with near-infrared light, unlike the image seen by the human eye
[Figure 1(a)], the deoxidized hemoglobin in the palm veins absorbs this light, thereby reducing the reflection rate and
causing the veins to appear as a black pattern [Figure 1(b)]. In vein authentication based on this principle, the region used for
authentication is photographed with near-infrared light, and the vein pattern is extracted by image processing [Figure 1(c)]
and registered. The vein pattern of the person being authenticated is then verified against the pre-registered pattern.

Working process:
The scanner makes use of a special characteristic of the reduced hemoglobin coursing through the integrated optical system
in the palm vein sensor uses this phenomenon to generate animage of the palm vein pattern and the generated image is
digitized, encrypted and finally stored as a registered template in the database. The parameters a false rejection rate (FRR)
and a false acceptance rate (FAR) is to be calculated for performance measurement. Also,if your profile is registered with
your right hand, don't log in with your left, the patterns of an individual's two hands differ. And if you registered your
profile as a child, it’ll still be recognized as you grow, as an individual's patterns of veins are established in utero.
1. Data from people ranging from 6 to 85 years old including people in various occupations in accordance with the
demographics realized by the Statistics Center of the Statistics Bureau.
2. Data about foreigners living in Japan in accordance with the world demographics released by the United Nations.
In addition to being contact less and thereby hygienic and user-friendly in that theuser does not need to physically touch a
surfaceand is free of such hygiene concerns, palm vein authentication is highly secure in that the veins are internal to the
body and carry a wealth of information, thereby being extremely difficult
toforge.Also proposed a hierarchical combination scheme for a multimodal
identification system. Kittler etc have experimented with several fusion
techniques for face and voice biometrics, including sum, product, minimum,
median, and maximum rules and they have found that the sum rule
outperformed others. Kittler et al. note that the sum rule is not significantly
affected by the probability estimation errors and this explains its superiority.
Hong and Jain proposed an identification system based on face and
fingerprint, where fingerprint matching is applied after pruning the database
via face matching. Ben-Jacob et al. considered several fusion strategies, such
as support vector machines, tree classifiers and multi-layer perceptron, for
face and voice biometrics. The Bayes classifier is found to be the best
method. Ross and Jain combined face, fingerprint and hand geometry
biometrics with sum, decision tree and linear discriminant-based methods.
The authors report that sum rule outperforms others.
Result of experiments:
As a result of the Fujitsu research using data from 140,000 palms (70,000 individuals), Fujitsu has confirmed that the FAR is
0.00008% and the FRR is 0.01%, with the following condition: a person must hold the palm over the sensor for three scans
during registration, and then only one final scan is permitted to confirm authentication. In addition, the following data has
been used to confirm the accuracy of this technology: data from 5-year to 85-year old people of various backgrounds based
on statistics from the Ministry of Internal Affairs and Communications of Japan’s population distribution; data from
foreigners in Japan based on the world population distribution announced by the U.N.; data of the daily changes of Fujitsu
employees tracked over several years; and data of various human activities such as drinking, bathing, going outside, and
waking up..
Contact Less Palm Vein Authentication Device:
A number of studies showing the advantages of multimodal biometrics fusion have appeared in the literature.
BrunelliandFalavigna used hyperbolic tangent for normalization and weighted geometric average for fusion of voice and
facebiometrics.They also proposed a hierarchical combination scheme for a multimodal identification system. Kittler et al.
have experimented with several fusion techniques for face and voice biometrics, including sum, product,minimum,median,
and maximum rules and they have found that the sum rule outperformed others. Kittler et al. note that the sum rule is
notsignificantlyaffected by the probability estimation errors and this explains its superiority. Hong and Jain proposed an
identification system based on face and fingerprint, where fingerprint matching is applied after pruning the database via face
matching. Ben-Yacoub et al. considered several fusion strategies, such as support vector machines, tree classifiers and
multi-layer perceptron, for face and voice biometrics. The Bayes classifier is found to be the best method. Ross and Jain
combined face, fingerprint and hand geometry biometrics with sum, decision tree and linear discriminant-based methods.
The authors report that sum rule outperforms others.
PRE-PROCESSING
Region of Interest Segmentation:
The acquired palm vein images are firstly normalized to minimize the rotational, translation and scale changes.
Therefore, to make the identification processeffective and efficient, it is necessary to construct a coordinate system
that is invariant/robust to (or nearly) those variations. It is intuitive to associate the coordinate system with the palm
itself,and thus the web between index finger and middle finger together with the web between ring finger and
little finger were utilized as the reference points/line to build up the coordinate system .

where LROI denotes the side length of ROI, LD denotes the distance between the ROI and the reference line, and
LW represents the distance between the two webs, α and β are the factors that control respectively the location and
size of theROI. The present approach is very similar to the method used in [5], however, our computations are simplersince
no additional sampling is employed. After this segmentation, ROI is resized to a fixed size (128 × 128 pixels in our
experiments) to facilitate the identification processes.
Image Enhancement :
Since the palm vein images employedin our work were acquired under near infrared illumination (NIR), the
image appears dark. Therefore to achieve better visualization of vascular and surface details, contrast enhancement is
required. We firstly estimate the background intensity profiles by dividing the image into slightly overlapped 32 × 32
blocks (3 pixel overlapping between two blocks to address the ‘blocky effect’), and the average values of each block are
calculated. Subsequently, the estimated background intensity profile is resized to the same size as the original image
using bicubic interpolation and then is subtracted from the original ROI image. Finally, the histogram equalization
was employed to obtain the enhanced image. The comparison between figures suggests that the enhancement scheme
has been quite successful in enhancing the local surface details.
Block diagram for the personal identification using multiple representations of palm vein images.

Advantages of using the palm:
In addition to the palm, vein authentication can be done using the vascular pattern on the back of the hand or a finger.
However, the palm vein pattern is the most complex and covers the widest area. Because the palm has no hair, it is easier to
photograph its vascular pattern. The palm also has no significant variations in skin color compared with fingers or the back
of the hand, where the color can darken in certain areas.
VEIN PATTERN RECOGNITION
Nearly any part of vein in human body (such as retinal vein, facial vein, veins in hand) could be used for personal
identification, but veins in hand are always preferred [6]. It is usually an uncovered part. Veins in hand are closer to the
surface than other organizes, so the traits can be easier detected by low-resolution cameras. In this paper, vein in hand is
involved, finger vein, palm vein, wrist vein and dorsal hand vein, and each of them offers stable and unique biometric
features.
Finger Vein Authentication System captures images of the vein patterns inside your finger. These, like other biometric
patterns are unique – but significantly, because they are inside your body, they are virtually impossible to replicate. The
method works by passing near-infrared light through the finger. This is partially absorbed by the hemoglobin in the veins,
allowing an image to be recorded on a CCD camera underneath. Then, it takes around 0.5 of a second to match your vein
patterns to those previously digitized, compressed and captured on a smart card. The process is remarkably accurate – there’s
only a 0.0001% chance of someone passing off their vein patterns as yours, as accurate as established iris systems. And, of
course, for additional security, you can always record the vein patterns in more than one finger.
Finger vein authentication process

IMAGING PRINCIPILE
As veins are internal, their structure cannot be discerned in visible light. Based on the kinds of light of acquisition, a vein
image can be classified as X-ray scanning, ultrasonic scanning and infrared scanning. X-ray and ultrasonic are used to
capture vein images in medical treatment, but they are not used in identification due to the health case. Until now,
researchers used infrared imaging for personal identification. Infrared (IR) is electromagnetic radiation whose wavelength is
longer than that of visible light, and Infrared light has a range of wavelengths lies between about 750nm and 1mm, just like
visible light has wavelengths that range from red light to violet. Infrared is commonly divided into 3 spectral regions: near,
mid and far-infrared light, but the boundaries between them are not agreed upon. There are two choices that focuses on
imaging of vein patterns in hand by infrared light, the far-infrared (FIR) imaging and the near-infrared (NIR) imaging, which
are suitable to capture human bodies images in a non-harmful way. Some papers had discussed the principle of the FIR and
NIR imaging methods. In the FIR method, superficial human veins have higher temperaturethan the surrounding tissues. For
NIR light method, the principle could be explained by photobiology. In biology, there is a “medical spectral window”, which
extends approximately from about 740 to 1100 nm. The light in this window could penetrate deeply into tissues. Because
blood and surrounding tissues have different effect on the NIR light, we could use a CCD camera with an attached IR filter
to capture images in which vein appears darker.

FIR Way
The human body temperature is about 36.85°C, and the temperature of surface of human veins is higher than that of the
surrounding parts. Therefore when the FIR light irradiates hand, the hand vein structure is thermally mapped by an infrared
camera at room temperature. The captured image shows a gradient of temperature between surrounding tissues and the back-
of-hand veins.
In literature , it is proved that the captured FIR
image of the back of hand has good quality,
which means containing more useful
information, but FIR vein image at palm and
wrist have poor quality. Whilst this method
deeply affects by the humidity and
temperature of surrounding, as well as the users’
perspiration does.
NIR Way
Near infrared wavelength is between about 700 nm to 1400 nm, and we can use the same observing methods as that used for
visible light, except for observation by eye. The NIR light is not thermal. NIR scanning device cannot penetrate very deep
under the skin therefore the device will recognize the superficial veins and rarely the deep veins. In the NIR way, the light of
specific wavelength is almost completely absorbed by the deoxidized hemoglobin in vein while almost penetrated the
oxidized hemoglobin in the arteries. Oxygenated and deoxygenated hemoglobin absorb light equally at 800 nm, whereas at
760 nm absorption is primarily from deoxygenated hemoglobin . Then the veins appear as dark areas in an image taken by a
CCD camera. Near-infrared (NIR) spectroscopy is a noninvasive technique that uses the differential absorption properties of
hemoglobin to evaluate skeletal muscle oxygenation.
NIR method is not a temperature based technique since normal body temperature or surrounding temperature cannot
interfere with this method. The FIR method is often used in hand-dorsa vein imaging, and NIR method can be used in all
veins imaging in hand. In order to benefit the processing, the captured images are always the grayscale image.
Vein pattern extraction
Because the temperature, illumination, locus and angle vary each collection, the captured digital picture varies each time. In
order to provide ‘better’ input for automated image processing and realize a robust system against some fluctuation, some
form of normalization should to be done a forehand. Conventional preprocessing algorithms can do this work. Then the vein
patterns are extracted after noise reduction and normalization. Several algorithms have been carried out to separate the vein
patterns from the image background. The captured images contain shading, noise and vein patterns, moreover, the vein
patterns are not salient. The more the information of veins is extracted and preserved, the better the accuracy is. So the
appropriate processing extracting the vein patterns is important for the authentication system. Recently vein of hand
extraction algorithm has been widely studied. Wherever the veins are, in finger, wrist, palm or the back of hand, the various
forms of vein patterns extracting algorithms usually fall into four broad categories: tracking-based, transform-based,
matched filter method and thresholding method. Here we will describe some work on each of these areas.
Tracking-based
The tracing algorithm is based on repeated line tracking the vein from initial seed-point in the captured NIR image, moving
pixel by pixel along the dark line in the cross sectional profiles [11]. In figure6, there is a certain position ‘s’, and the left is
its cross sectional intensity profile of finger vein image. Tracking direction is determined by the position of deepest point in
the cross sectional. This method can extract vein patterns from low quality NIR images, but it is sharply affected by the
temporal change of widths of veins.

Transform-based methods:
The captured image always has low contrast and contains noise, so contrast enhancement and noise reduction are crucial in
ensuring the quality of the subsequent steps. Transform based methods can convert image to a certain domain in which it is
more suitable for extracting the patterns. Wavelet, which supports multi-resolution analysis, is one of the appropriate
methods for vein structure and feature extracting. The wavelet multi-resolution approach employs a wavelet basis to analyze
at different resolutions and increase resolution from coarse to fine, so the content of image in each scale can be understood.
Vein patterns are well structured objects consisting of line-like veins and areas in between. The wider veins can be analyzed
in the lower resolution, and the thinner veins can be analyzed in the higher resolution. In paper [12], dyadic wavelet
transform is adopted to extract finger vein patterns from background. Image is transformed from spatial domain to wavelet
domain, and the grayscale image is changed into wavelet coefficients, which contain vein patterns wavelet coefficients and
noise wavelet coefficients. The vein pattern variance of coefficients is larger than that of noise, and with the increasing of
wavelet scale, the noise variance decreases.
Matched filtermethod:
By observing the cross sectional profiles of vein patterns, some researchers proposed an intensity profile model to detect
vein patterns. Several models have been presented to describe the cross sectional profile of vessel [13-15]. The gray-level
profile of the cross section is approximated a Gaussian shaped curve, which is prevalent used, whilst the matched filter is
utilized to detect vein patterns. Since vein patterns may appear in any orientation, a set of cross sectional profiles in
equiangular rotations is employed as a filter bank.

Thresholding method:
Intensity thresholding is usually utilized to obtain a better representation of shapes of the vein patterns. In the IR image the
different location has different intensity values of the veins. Hence applying a single global thresholding is inappropriate.
Via adaptively adjusting local thresholding, we can choose different threshold values for every pixel in the image based on
the analysis of its surrounding neighbors [9], then, separate the vein patterns from the background, after that the desired vein
image is extracted.
Pattern matching:
The extracted vein patterns of the input image can directly be compared with the templates. A certain distance is defined to
calculate the similarity between the template and the input patterns. But when the template is not small, the comparing time
lasts long. After pattern extracting process, most systems are interesting in eliciting skeletonisation of thevein patterns. Then
Vessels can be represented by the number of intersections, the total segment length, the longest segment, and the angles
found in the image, the distribution of the vein, and other statistical features. Hausdorff distance, SVM, and nearest neighbor
are adopted as matching algorithm by researchers.
Database:
Recently, significant work is continuously being done in vein recognition algorithms both in academy and industry.
However, the conclusion of each work is usually achieved on their own databases but not the sharable databases. Large
sharable vein databases are required to evaluate and compare various algorithms. Vein pattern data collection is an expensive
and time-consuming work. There are some inconveniences in large databases collection. Firstly, it is expensive both in terms
of money and time; secondly, it is tedious for both the technicians and for the volunteers; thirdly, due to privacy information,
it is difficult to share data with others. Though the real images cannot be replaced, the synthetic vein images have proven to
be a valid substitute for real vein for design, benchmarking and evaluation of vein recognition systems. A synthetic like-vein
image method is requested. Based on the cross sectional profiles of vein patterns, the vein pattern can be synthesized in
semiautomatic way as figure10. Firstly, lines which look like vein patterns were drawn by hand. Secondly, according to the
different cross sectional profile models, the like-vein patterns can generation by programs.

Application of vein recognition system andfuture work
Vein recognition technology has some fundamental advantages over fingerprint systems. Vein patterns in hand are biometric
characteristics that are not left behind unintentionally in everyday activities. Vein patterns of inanimate bodily parts become
useless after a few minutes. Hence, nowadays, vein recognition system is regarded a mainstream technology. IBG expects it
to play a larger role and comprise more than 10% of the biometric market [18]. Nearly all major vein authentications are
manufactured in Japan and Korea, and the
application of these manufactures is used in Asia. In Japan and some other countries, such
products spread particularly in the financial sector.
Fig: (a) Finger Vein device; b) Finger Vein ATM; c) Palm Secure by Fujitsu
The recent launch of vein recognition technology is successful. Nevertheless, some research issues need to be addressed in
future. For one thing, work continued across the vein imaging device to make it cheaper, more accurate and robust. For
another thing, the quality of vein IR image is affected by the relationship of intensity between the IR light and the ambient
light, as well as the ambient temperature. Moreover, the sharable large databases should be founded for a thorough
evaluation on the efficacy of different vein recognition algorithms. Lastly, vein trait is able to conjunct with other biometrics
in a multi-modal system.
MARKETING
A reliable biometric system, which is essentially a pattern-recognition that recognizes a person based on physiological or
behavioral characteristic , is an indispensable element in several areas, including ecommerce(e.g. online banking), various
forms of access control security(e.g. PC login), and so on. Nowadays, security has been important for privacy protection and
country in many situations, and the biometric technology is becoming the base approach to solve the increasing crime.
As the significant advances in computer processing, the automated authentication techniques using various biometric
features have become available over the last few decades. Biometric characteristics include fingerprint, face, hand/finger
geometry, iris, retina, signature, gait, voice, hand vein, odor or the DNA information, while fingerprint, face, iris and
signature are considered as traditional ones. IBG Biometric Market by Technology Due to each biometric technology has its
merits and short coming, it is difficult to make a comparison directly. Jain et al. have identified seven factors, which are (1)
universality, (2) uniqueness, (3) permanence, (4) measurability, (5) performance, (6) acceptability, (7) circumvention, to
determine the suitability of a trait to be used in a biometric application.

Vein pattern is the network of blood vessels beneath person’s skin. The idea using vein patterns as a form of biometric
technology was first proposed in 1992, while researches only paid attentions to vein authentication in last ten years. Vein
patterns are sufficiently different across individuals, and they are stable unaffected by ageing and no significant changed in
adults by observing. It is believed that the patterns of blood vein are unique to every individual, even among twins.
Contrasting with other biometric traits, such as face or fingerprint, vein patterns provide a really specific that they are hidden
inside of human body distinguishing them from other forms, which are captured externally. Veins are internal, thus this
characteristic makes the systems highly secure, and they are not been affected by the situation of the outer skin (e.g. dirty
hand). At the same time, vein patterns can be acquired by infrared devices by two ways, noncontact typeand contact type. In
the case of non-contact method, there is no need to touch the device, and therefore it is friendly to individuals in the target
population who utilize the systems. In the contact type, the collection type is the same as fingerprint which has already been
accepted by most people. From the customer’s point of view, the authentication system is not only high accuracy level for
security but also easy to enroll. Vein patterns serve as a high secure form of personal authentication as iris recognition (Iris is
known for high accurate rates of authentication, but it is regarded unfriendly by users due to the direct application of light
into their eyes), and serve as a convenient form as fingerprint recognition. On account of the several advantages, vein
authentication is not only interested in lab researchers but also in industries, and the products perform well in tests of the
International Biometric Group (IBG). Recently, vein recognition appears to be making real headway in the market, and
considered as one of the more ’novel’ biometric, which is called ‘the Fourth ‘Biometric’.
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