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VEIN RECOGNITION SYSTEM (2012-13)

VEIN PATTERN RECOGNITION

Vein pattern recognition is one of the newest biometric techniques researched today. While the

concept behind the method is quite simple, there are various challenges to be found throughoutthe design and implementation of a vein scanning device concerning the hardware lightingsystem and the actual algorithms used for processing the acquired images

WHY VEIN DETECTION ?

The vein is hidden inside the body and is mostly invisible to human eyes, so it is difficult to

forge or copy. Uniqueness and permanence of the pattern

Non-contact detection procedure

The biometric parameter is hidden from general view

The vein pattern is intricate enough to allow sufficient criteria for positively detectingvarious sub ects even identical twins accurately to discover vein pattern recognition.

AVAILABLE METHODS CLASSIFICATION

!vailable vein recognition methods can be classified into two categories according to the processing principle. "ne is based on low-level image processing and the other is based on high-level image processing . #uppose in this paper, the former and latter category methods are calledtype $ and $$ methods, respectively.

Typ ! "# $%"& ' % !

%ehavioral-based methods

&hysiological-based methods

P*V*P*I*T B+DHGAON Page 1
http://www.scialert.net/asci/result.php?searchin=Keywords&cat=&ascicat=ALL&Submit=Search&keyword=image+processinghttp://www.scialert.net/asci/result.php?searchin=Keywords&cat=&ascicat=ALL&Submit=Search&keyword=image+processinghttp://www.scialert.net/asci/result.php?searchin=Keywords&cat=&ascicat=ALL&Submit=Search&keyword=image+processinghttp://www.scialert.net/asci/result.php?searchin=Keywords&cat=&ascicat=ALL&Submit=Search&keyword=image+processing

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Typ I & '," !.

$n the type $ methods, traditional image processing techniques, such as image

enhancement, 'i et al ., ())*a , b+ ()) + 'i and &an, ())* , ()) segmentation, edge detection,etc. are used. /or e0ample, proposed some methods of vessel segmentation for the utili1ation inseparation of the retinal vascular graph in arteries and veins. %esides spatial domain operations,some transforms are also employed as supplementary tools, such as 2iscrete /ourier Transform32/T , discrete cosine transform 324T , 2iscrete Wavelet Transform 32WT , 5adon transformetc. !s high correlation e0ists among neighboring image pi0els, most of these transformse0hibits high efficiency in energy compaction of highly correlated data and thus can concentratethe vein image content in a few coefficients in transform domain. Nevertheless, some transformsare robust against affine transforms 3e.g., rotation . This is useful for pose variation occursduring vein acquisition.

#imilar as fingerprint, global and local features are contained in each vein pattern. Toe0act the global features, the graph-based methods can be used."ne of the typical Type $methods is $n it, the infrared image of hand dorsal vein is captured for analysis and the

bifurcation and ending points are e0tracted as minutiae features. !veragely, 67 minutiae pointsincluding * bifurcation and 8 ending points are included in each vein image. Thus the tas9 isreduced to e0tract and match these 67 feature points.

Typ II & '," !. The theft and fraudulent use of my credit cards and ban9 cards in 6 7 at :oda9s!nnesley

plant in ;ngland led me to invent. $n contrast, in the Type $$ methods, personal authentication or identification is considered as a problem of pattern classification. The common characteristics issome artificial intelligence or machine learning techniques.$ntroduced manifold learning, one of the machine learning techniques, is for vein recognition for the first time. proposed a driver identification system using finger-vein technology proposed in which radial basis function 35%/networ9 and &robabilistic Neural Networ9 3&NN are employed as the classifiers. ;0perimentalresults show the average identification rate of &NN networ9 is no less than .(

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HADRWARE SET+P

The hardware setup has a crucial role in the acquisition of vein images. Two aspects can beunderlined here=

The actual camera used for ta9ing the snapshot has only one important parameter, the responsetonear infrared radiation. #patial resolution and frame rate are of lower importance since for theacquisition of a vein pattern a still image is required and the details are easily seen even at alower resolutionThe design of the lighting system is one of the most important aspects of the image acquisition

process. ! good lighting system will provide accurate contrast between the veins and thesurrounding tissue while 9eeping the illuminations errors to a minimum.!s the hardware implementation of this pro ect is very costly and for hardware design weneed .N;T and other related language coding so we are processing the sample images.

H/ / ! ' p.

/igure shows the how the actually hand veins are scanned from different layer of operations.


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SOFTWARE SET+P

B/ " 4 "4 MATLAB /4 ', I&/ P " !!%4 T""5$"6

>!T'!% is a high-performance language for technical computing. $t integratescomputation, visuali1ation, and programming in an easy-to-use environment where problems andsolutions are e0pressed in familiar mathematical notation.

Typical uses include the following=

? >ath and computation

? !lgorithm development

? 2ata acquisition

? >odeling, simulation, and prototyping

? 2ata analysis, e0ploration, and visuali1ation

? #cientific and engineering graphics

? !pplication development, including building graphical user interfaces

>!T'!% is an interactive system whose basic data element is a matri0. This allowsformulating solutions to many technical computing problems, especially those involving matri0representations, in a fraction of the time it would ta9e to write a program in a scalar non-interactive language such as 4.

The name >!T'!% stands for >atri0 'aboratory. >!T'!% was written originally to provide easy access to matri0 and linear algebra software that previously required writing/"5T5!N programs to use. Today, >!T'!% incorporates state of the art numericalcomputation software that is highly optimi1ed for modern processors and memory architectures.

$n university environments, >!T'!% is the standard computational tool for introductoryand advanced courses in mathematics, engineering, and science. $n industry, >!T'!% is thecomputational tool of choice for research, development, and analysis. >!T'!% iscomplemented by a family of application-specific solutions called toolbo0es. The $mage&rocessing Toolbo0 is a collection of >!T'!% functions 3called >-functions or >-files thate0tend the capability of the >!T'!% environment for the solution of digital image processing

problems. "ther toolbo0es that sometimes are used to complement the $mage &rocessingToolbo0 are the #ignal &rocessing, Neural Networ9s, /u11y 'ogic, and Wavelet Toolbo0es.ACT+AL PRACTICAL SET+P


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FLOWCHART .-

/igure. %asic steps in /eature ;0traction


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F /' 6' / '%"4 A5 " %',& !' p!

This are different algorithm steps in the feature e0traction algorithm. These steps can be ta9es place as follows.

R/ %&/ E ' '%"4 A /p'%7 R &"7/5 "# ', T,%4%4

L" p/!! #%5' ', !,"5 !&/55 "$8 '!

F% *F /' 6' / '%"4 /5 " %',&

6. 4onsecutive contrast operations in con unction with a low-pass Eaussian filter are used toenhance the image of the vein model

(. Threshold is applied thus creating a binary image outlining the vein pattern.

7. The resulting image suffers more transformations. ! thinning algorithm is applied and all linesare converted into 6 pi0el-width lines in order to compensate for the effects of aging, temporaryvessel constriction or dilation, and other medical factors that can modify the width of the veins.This is also necessary if the measurement data has been collected at various timestamps and thevein pattern has modified in si1e 3usually a global increase of the pattern .

F. "ne of the most important problems of a feature e0traction algorithm is the preservation of theconnectivity of the vein model since a regular edge detection technique is not optimi1ed forfinding vein structures. #everal sub-algorithms can be used to find the lines of the model, either

by the same technique used in fingerprints 3ridge finding or by following the connectivity ofeach line. 2ifferent algorithms will differ in terms of comple0ity and therefore the neededcomputational resources will vary.


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This are the different steps in feature e0traction algorithm. These are as follows.

2ata acquisition

$mage preprocessing

$mage segmentation

$mage enhancement

'ow pass filtering

!daptive thresholding

$mage thining


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FLOW CHART


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R ! 5' A4/5y!%!

!fter all these processes in the feature e0traction algorithm this is the last stage.$n this stage theimage which is obtained after all the processes is compared with database and human

identification is carried out.

The personal identification technique has been tested only for the database image.'argeshareable large databases should be required for large efficiency of different vein recognitionalgorithms.@and vein biometric can be used as high secure authentication system in fusion withother biometric.


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ADVANTAGES OF VEIN RECOGNITION

$ts authentication information is hardly corrupted.

$t is difficult to replicate veins because they are not seen with the na9ed eye.The vein patterns areunique to each individual and apart from si1e+ the pattern does not change over time.! contactfree technology.

Vein recognition is a fairly recent technological advance in the field of biometrics.$t providesvery high levels of security.

4ost- Non-harmful, near infra-red lighting is employed.

Non-invasive, socially acceptable alternative to fingerprinting and retinalscanning

/ast, easy-to-use, and discreet

Very low false re ect rate

4ompact reference pattern 3F)) bits Not easily replicated.

DISADVANTAGES

$t is not yet fully proved that biometric authentication information is permanent and

unique. ;0pensive and huge si1e.


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APPLICATION AND F+T+RE SCOPE

!pplication of vein recognition system and future wor9Veinrecognition technology has somefundamental advantages over fingerprint systems.

Vein patterns in hand are biometric characteristics that are not left behind unintentionally ineveryday activities.

Vein patterns of inanimate bodily parts become useless after a few minutes. @ence, nowadays,vein recognition system is regarded a mainstream technology.

$%E e0pects it to play a larger role and comprise more than 6)< of the biometric mar9et . Nearly all ma or vein authentications are manufactured in Gapan and :orea, and theapplication of these manufactures are used in !sia. $n Gapan and some other countries, such

products spread particularly in the financial sector.

The recent launch of vein recognition technology is successful. Nevertheless, some researchissuesneed to be addressed in future.

/or one thing, wor9 continued across the veinimaging device to ma9e it cheaper, moreaccurate and robust. /or another thing, the quality of vein $5 image is affected by therelationship of intensity between the $5 light and the ambient light, as well as the ambienttemperature.

>oreover, the sharable large databases should be founded for a thorough evaluation onthe efficacy of different vein recognition algorithms. 'astly, vein trait is able to con unct withother biometrics in a multi-modal system.the vein recognition system is also used in followingapplications.


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!tm and ban9ing, #chools and colleges ,&ersonal identifications.

CONCL+SION

! new vein based user identification system for personal identification and authodificationconsumer electronic devices. The system provides effective and efficient features using /eaturee0traction algorithm.#ince from these technique we can achieve high level of security.


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REFERNCES

6 . #. &rabha9ar, #. &an9anti, !. :. Gain, A%iometric 5ecognition = #ecurity and &rivacy4oncernsB, $;;; #ecurity H &rivacy, >archI!pril ())7 pp 77-F(

(. User $dentification %ased on /inger-vein &atterns for 4onsumer ;lectronics 2evices

2esong Wang, >ember, $;;;, Gianping 'i, and Eo9han>emi9

@. 'ee, #.-@. 'ee, T. :im, and @. %ahn, A#ecure user identification for consumer electronicsdevices,B $;;; Trans.

4onsumer ;lectron., vol. JF, no. F, pp. 6* -6 )(, Nov. ()) .

7. !. :. Gain, '. @ong, #. &an9anti, and 5. %oole, A!n identityauthentication system usingfingerprints,B &roc. $;;;, vol. J,

no. , pp.678J-67 , #ept. 6 *.

F. 2. Khang, W.-:. :ong, G. Lou, and >. Wong, A"nline palmprint identification,B IEEE Trans. Pattern Anal. Mach. Intell. , vol. (J,

no. , pp. 6)F6-6)J), #ept. ())7

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