Authentication Using Hand Vein Pattern

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303

Authentication Using Hand Vein Pattern Yuvaraj S1 1

PG Scholar/Applied Elecronics, Department of ECE, Bannari Amman Institute Of Technology, yuvarajnandha39@gmail.com

John Clement Sunder A2 2

Assistant professor Department of ECE, Bannari Amman Institute Of Technology johnclementa@bitsathy.ac.in

Abstract-This paper describes and implements an authentication resolutionmistreatmentstatistics, digital certificates and sensible cards to unravelthe protectiondownsidewithin the authentication method. The primaryhalfmay be a general introduction to the subject; the second may be atemporarysummaryregardingmistreatmentstatistics, a lot ofprecisely hand vein pattern. The third half presents a way of extracting the pattern vein of the rear of the hand additionallya way to match 2 templates. The fourth presents the 2 necessary phases in any authentication system: the enrolment and therefore the authentication. A projected authentication protocol is delineated too. The twenty percent generalize the attainable attacks and vulnerabilities during abiometric identification system and it additionally shows however our system is ready to avoid them .The sixth half talks regarding the implementation of the applying. Finally, within the conclusion, we tend to tried to summarize our work and prove the advantages of mistreatmentthis technique.

Keywords- Biometrics, vein recognition, filtering infrared light. properties: universality, uniqueness, collectability, storability, performance.

1. INTRODUCTION

permanence,

2.2 Hand Vein Pattern As A Biometric Pattern

Vein recognition is a fairly recent technological advance in the field of biometric. It is used in hospitals,lawenforcement, military facilities and other applications thatrequire very high level of security. Vein recognition biometric device can alsobeused for PC login, bank ATMidentification verification, and many other applications such as opening car doors. This kind of biometrics is particularly impressive and promising technology because it requires only a single-chip design, meaning that the units are relatively small and cheap. The ID verification process is very fast and without trace.Using a light-trans mission technique, the structure of the vein pattern can be detected, captured and subsequently verified. Of the many new bio metric technologies such as DNA, Iris recognition, car and body odour recognition; vein recognition with its own unique characteristics and advantages is now emerging has one of the fastest growing technologies. Vein recognition is the newest type of biometric technology and is quickly moving from labs to wide spread commercial development.

a) Vein pattern and NIR imaging In order to collect the vein pattern, an image of it is needed. The structure of the vein pattern can be obtained either through thermal imaging also called Far Infra-Red,or through NIR imaging. Since a thermal camera costs several thousandUS dollars, NIR is a more viable option for building a cheap capture setup. That is why this project is going to focus on NIR imaging of the hand. The human body radiates infrared light, but only in the range of3000 14000 nm, with an intensity high enough to be picked up (lOmW=cm2).Natural radiation in the NIR, on the other hand, is not strong enough to be detected by devices [1]. Therefore if NIR is used, the hand needs to be irradiated and it is because of the absorption and reflection properties of the body that a useful image can be captured.

3.Pattern Vein Extraction & Matching 2. BIOMETRICS AND HAND VEIN PATTERN

Pattern recognition systems can exist in endless forms, dealing with problems in many different fields and using different methods to achieve their goals. Inspite of this, most, if not all, systems tend to follow the same overallstructure. The structure of a hand vein pattern recognition system can be summed up by the general block diagram shown in fig.1.

2.1 Biometrics Biometrics is the automated use of physiological or behavioural characteristics to determine or verify identity.

Any human physiological or behavioural characteristic could be a biometric feature, provided it has the following desirable

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303 important to get a uniform illumination. It has been suggested that a convex surface such as the back of the hand, can be optimally lit if we put the IR LEOs under the hand. [3].To make a better illumination some IR LEOs were added in front of the hand to avoid shadows and upper than camera to avoid interferences.

Based on these spatial requirements, the design shown in fig.2 was made.

Fig.1 Pattern recognition system diagram.

3.1 Image Acquisition and Preprocessing The purpose of this section is to create a capture setup that is efficient and has a low-cost. A cheap webcam (SeleclinePPW-I0) was chosen to be used for taking the pictures of the hand. The camera has been modified so it can capture NIR images. A captured image of a hand in the NIR spectrum shows the veins in black and the skin in white.

3.1.1

Fig.2 Illustration of the capture effect

c) Hand Constraints &Region Interest Afteran image has been acquired the ROI needs to be extracted. The hand is constrained in order to restrict movement; making axed ROI feasible. There is several ways to constraint the hand to prevent rotation and translation. A hand grip and a rod are used where the person rests his/her arm in order to prevent any rotation of the hand. To prevent movements,two pins have been added, one onthe handgrip, theother ontherod. By placing the arm as shown in the setup illustrated in Fig.3, most movement and rotation is prevented. The ROI is then obtained as a 320x360 rectangle with upper left come rat pixel (200,100).

Capture Setup

a) Modification of thecamera Most webcams have a CMOS image sensor, which is sensitive to both visible light and NIR light. Most are also sold with a filter which blocks all NIR light in order to improve quality of the image in the visible spectrum.

However the camera used for this project had this filter removed, and was thus already sensitive to both visible and NIR light. So in order to capture only images in the NIR spectrum, visible light must be blocked otherwise it would reduce contrast between veins and background.

This would reduce usability of the image.

The easiest and cheapest way to block the visible light is to use pieces of a colourphotographic negative as filter. The response curve for a filter made from an exposed Kodak colourfilm shows that the film negative is a very good filter for this purpose. After developing the film, it is cut into small pieces, setting the size of the webcam lens and axed on it. Such a modified webcam produces pictures in NIR spectrum.

Fig.3. Visible light image and IR image

b) Illumination Different factors influence the image quality and an important one is the lighting conditions. The hand needs to be illuminated in order for the device to obtain an image of the hand vein pattern. Hence Infra-Red OR) Light Emitting Diodes(LEOs) were added tothesetup. The position of these LEOs is very

Thanks to the constraints the captured image will be approximately one of the same regions each time with just few variations .

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303 While the use of IR image capturing makes the veins stand out more clearly, it is often necessary to furtherimprovethe contrast before segmenting the image.

A simple butvery effective method to do this ishistogram stretching. This method exploits the fact that most images pixel values don't span the entire range of possible values from 0 to 255.

In thesimplest form, a histogram stretching algorithm uses the lower limit aand the upper limit b to transform the colors in the image. All color values in between a and b will be transformed so they span the entire range from 0 to 255. The colors below a and above b will be set to 0 and 255 respectively. The first step is to find c which is the mean of a and b.

3.1.2 PreProcessing Methods This subsection describes the methods that are used to preprocess the input images. The preprocessing step serves two main purposes. The first is smoothing and noise removal. Since the images are captured using a modified consumer webcam, considerable noise can occur in the images. Gaussian and median filters are used to remedy the effect of this noise. The second is contrast enhancement. This is necessary as the vein pattern can be faint. Histogram stretching is used to add contrast between the veins and the background.

a) Smoothing & Noise Removal

Then every pixel in the image is transformed as follows: There are many ways to deal with noise in images.

Some methods exploit the fact that the noise is a random variable with 0 mean that is added to the image. Thus by averaging the image, the effect of the noise is canceled out. Unfortunately this produces a smeared version of the image where small veins might be lost in the process.

A different approach is to exploit the fact that the noise tends to have high frequencies, while the important features in the image do not.

Using this method, the color space is stretched equally around the mean of the two limits. An extension is to let c be a variable in between aand b. This allows for uneven stretching around c which is a simple form of gamma correction. Finally, the output preprocessed image, is sent to the segmentation block.

b) Gaussian Filter A Gaussian filter is a smoothing filter based on the Gaussian distribution. It is suitable for image noise removal because it acts as a low pass filter, attenuating high frequency noise while leaving the lower frequency features unchanged.

The low pass filter propertyof the Gaussianfilter can be seen from its Fourier transform which isitself aGaussian function.

This means that the filter attenuates rapid changes in an image, effectively smoothing it. The amount of smoothing depends on the chosen standard deviation () the higher () is, the smoother the resulting image will be.

Good results were obtained by setting ()-0.5.

c) Median Filtering Another source of noise in the images is hairs that show up as very thin dark lines. A way to remove these is to use amedian filter. The median filter works byreplacing pixel values with the medianvalue of that area. This is done by iterating through every pixel in the image and looking at its neighbors within a specified distance. These pixel values are then gathered and sorted. The value in the middle of the resulting set is then chosen to be the center pixels new value.

Fig.4. Histogram Equalization 3.1.4 Image Segmentation and Post Processing

3.1.3Contrast Enhancement withHistogram Stretching

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303 connectivity losses are increased if the subjects have either a thin or contracted vascular pattern. The Direction Based Vein Pattern Extraction (DBVPE) algorithm reduces connectivity losses, and hence improves the accuracy of the segmentation.

The algorithm is divided in two filters that emphasize the vein pattern: a Row Vascular Pattern Emphasizing Filter (RVPEF) and a Column Vascular Pattern Emphasizing Filter (CVPEF) which respectively does an extraction of the abscissa or ordinate vascular pattern.

Both filters are applied to an image, producing two outputs.

These are then combined in order to produce a final enhanced output. In [2], the coefficients of the emphasizing filters proposed are numbers in form of power of two in order to be able to perform the filtering process only using binary shifts. In order to increase the computation performance, the filter can be presented as two ID-arrays. It allows the performance of the filtering by using two 1D convolutions instead of a 2D convolution.

3.1.4.1 Image Segmentation The goal of segmentation is to simplicity and or change the representation of an image into something that is more meaningful and easier to analyze. The process of segmentation is crucial to the performance of the system. Some methods of segmentation have been proposed in the literature. We choose for the segmentation process two methods: Adaptive Local Threshold and Direction Based Vascular Pattern Extraction.

3.1.4.2 Post processing Post processing use methods to process the image after segmentation, in order to reduce the effect of undesired elements such as noise. This is done with morphological operators and a filter to remove small pixel blobs in the image.

The post-processing is composed by morphological operators: (opening and closing that remove noise and consolidate the pattern) and blob removal (it removes the blobs that are less than 5% of the largest blob in the image).

Fig.5. Segmented Image

a)Adaptive Local Threshold Thresholding creates binary image from greylevel ones by turning all pixels below a certain fixed value, called a threshold, to zero and all pixels above to one. Due to the fact that the grey-level intensity values of the veins vary across the image, global thresholding doesn't provide satisfactory results.

Therefore, local thresholding is a preferable binarization method.

Localadaptive thresholding selects an individual threshold for every pixel based on its local neighbors. The algorithm chooses different threshold values for each pixel based on the analysis of its surrounding neighbors.

The pixels are defined as being in a pixel's neighborhood if they lie in the coxco quadratic kernel, with the pixel in question as center. The threshold is found by calculating the mean of the neighboring pixel values.

b) Direction Based Vascular Pattern Extraction Fig.6. Segmented Image with Vein Pattern This method is not actually segmentation in itself. But it is used to improve the results of the local threshold segmentation. Conventionally the extraction of vein pattern does not take into consideration directional information, thereforesome losses inconnectivity of the pattern are likely to occur and it will lead to an incomplete representation of the pattern. The

3.1.4.3 FeatureExtractionand Recognition a) Feature extraction Two methods used for extracting features from a vein pattern. The first is thinning, which condenses the vein pattern until it is only one pixel wide. This method

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303 extracts the end thinnedveinpattern.

and

crossing

points

from

the

library, A Forge. Imaging. AForge.N ET framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence-image processing, neural networks, genetic algorithms, machine learning, robotics, etc. The framework is comprised by the set of libraries and sample applications, which demonstrate their features [6]. For the cryptographic work we used the Bouncy library Castle [7] cryptographic and built-in SystemSecurity. Cryptography library. The protection of the user personal data was realized by enveloping it in a zip file and a password protects it. This was done using Dot Net Zip library which is an easy-to-use, fast, free class library and tool set for manipulating zip files or folders [8].

b)Recognition For the recognition step we choose Delauny Triangulation method because it seems to be the most appropriate for ourneeds.

4. Authentication In the authentication phase all the exchanges between smart card and host must be secured in order to avoid attacks and identity thefts. When a user wants to authenticate to the system he must first insert his smart card in the card reader and provide his password. If everything is ok the system will start a secure transactionat the end of which it will receive the biometric certificate of the user. This biometric certificate is necessary to continue the authentication phase. After the system has the user template it can start the acquisition of the user hand vein image. Then it needs to process this image and in the final compare the results with the received ones using Modified Hausdorff Distance (described in part III, section C).If the distance is greater than the fixed threshold then the user will be rejected otherwise he will be authenticated.

6. Result The feature extraction algorithm.Thisalgorithm transformsthe binary segmented image,thatvariesforthe samehand according tothe variationofthe diameterofthe veins,into the globalshape ofthe veinpattern, calledaskeleton.

Itisfollowedbyapruning algorithm thatisusedinorder togetrid ofsmallunnecessarybranches i n the skeletonofthe veinpattern.Oncethe skeleton is fullyextracted,the recognitionismade usingDelauny triangulation method. From this, triangles will be formed from the set of points.The method is powerful because it gives an optimal triangulation in the sense that it maximizes the smallest angle in the set of generated triangles.

5. Implementation To implement a biometric authentication system we have to take in considerations a few aspects like security, memory usage, processing time, volume of computations, etc. These aspects are necessary because even if the system is a stronger machine it interacts with smart cards which have a limited memory and a small CPU. Before start programming, a biometric scanner was built in order to acquire the vein pattern of the back of the hand. After that an enrollment application was made and in the final two authentication applications was built, one for the system and the other one for the card. The authentication application for the user is necessary because we used a SD card inspite of a smart card and the first one doesn't haves a CPU and a RAM. For the implementation of the applications wechose as programming language C#. Also we used two libraries that deal with image processing in order to process our hand images and extract the pattern vein.

First and the most used in our project is Emgu CV. It is across platform .Net wrapper to the Open CV image processing library. It focuses mainly on real-time image processing [5]. We used this library in all stages of image processing less inblobs extractionand thinning phases where we used the second image processing

Fig.7. Delauny Triangulation

6.Conclusion Biometrics, such like vein recognition, refers to strategies for recognizing individual individualsbased mostly on distinctive physical and behavioral traits. Physiological statistics is one category of statistics that deals with physical characteristics and attributes that area unitdistinctive to people. Vein recognition could be aform ofstatisticswhich

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303 will be accustomedestablishpeoplesupported the vein patterns within the human finger. Several feel that vein recognition statisticswillturn out higher accuracy rates than finger print recognition and vein patterns area unitjust aboutnot possible to forge.

Author Profile:

Adding at this technology PIG infrastructure and store information on a wise card instead on infowe will increase the safety and additionallyproduce a stronger technology more durable to forge. The plannedtechnique for identification is very secure beneatha spread of attacks and may be used with a largeform of biometric traits. Because the authentication may beworn outperiod with the assistanceof accessible hardware, the approach is additionallysensible in several applications. The employment of sensible cards to carryencoding keys allows applications like biometric ATMs and access of services from public terminals.

7.References [1] A.M. Nadort. 'The hand vein pattern usedas a biometric feature". Free University, Amsterdam: Master's thesis, 2007...1.Clerk Maxwell, A Treatise onElectricityand Magnetism, 3rded. vol.2.Oxford: Clarendon,1892,pp.68-73.

[2]S.K.1m, S.W.KimandH.S.Choi."Adirection-based vascular pattern extraction algorithm for handvascularpatternverification".s.l.: ETRIjournal,vol.5, no.2,pp.101-108, 2003.

[3] Y. Zhao andM.Y. Shang. "Acquisition and Preprocessing of Hand VeinImage".s.1.: IEEE9781-61284-774-0, pp5727-5729, 2011.

[4]NSAR223.TokenLock/Unlock forTOKENEER. s.l. ¡G. PetersoWorking Notes, 1997.

[5]EmguCV.http://www.emgu.com. [Online] 2012 [6]AForge.http://www.aforgenet.comi.2012.

[7]BouncyCastle.http://www.bouncycastle.orglin dex.html.[Online] 2012 [8]DotNetZip.http://dotnetzip.codeplex.com/.

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[9]N.KRatha,LHConnell. andR.MBole."AnAnalysisofMinutiaeMatchingStre ngth".NewYork10532: IBMThomasJ.WatsonResearchCentre30 SawMillRiverRoadHawthorne, 2007 [10]lLWayman."TechnicalTestingandEvaluation of BiometricDevices". [book auth.]A. Jainetal.

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[11]C.Roberts.Biometricattackvectorsanddefences .2006

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1.

S Yuvaraj is currently pursuing Master of Engineering in Bannari Amman Institute of Technology, India,Ph:9629429643, E-mail:yuvarajnandha39@gmail.com

2.

A John Clement Sunder is currently working as Assistant Professor in Bannari Amman Institute of technology,India,Ph:9750645780, E-mail: johnclementa@bitsathy.ac.in