Address for Correspondence 1 P.G. Student (Computer and Communication), 2 Associate Professor

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1 Reseach Aticle BIOMETRIC AUTHENTICATION USING NEAR INFRARED IMAGES OF PALM DORSAL VEIN PATTERNS M.Rajalakshmi 1, R.Rengaaj 2 Addess fo Coespondence 1 P.G. Student (Compute and Communication), 2 Associate Pofesso Depatment of Electical and Electonics Engineeing SSN College of Engineeing, Rajiv Gandhi Salai, Kalavakkam , Chennai, Tamilnadu, India. ABSTRACT This pape poposes an impoved palm dosal (back of hand) featue extaction algoithm fo biometic pesonal authentication applications. The poposed method employs the existing database of nea Infaed (IR) images of palm dosal hand vein suface. The poposed system include: 1) Infaed palm dosa images database collection; 2) Detection of Region of Inteest (ROI); 3) Palm vein extaction by median filteing 4) Featue extaction using cossing numbe algoithm 5) Authentication using minutiae tiangulation matching. The input image is segmented using an optimum thesholding algoithm. The knuckle points ae used as key points fo the image nomalization and extaction of egion of inteest. The extacted ROI is pocessed to get the eliable vein patten and featues (minutiae) ae extacted using cossing numbe algoithm. The scoes fo pefoming authentication ae geneated based on minutiae tiangulation matching. KEYWORDS palm dosal vein, vascula biometics, knuckle points, thesholding algoithm, histogam equalization, median filteing, minutiae, cossing numbe, tiangulation. I. INTRODUCTION Recently, pesonal authentication has become an vital and high-demand technique fo secuity access systems. The secuity field uses thee diffeent types of identifications [1]: Something You Know a passwod, PIN, o piece of pesonal infomation (such as you mothe's maiden name); Something You Have a cad key, smat cad, o token (like a Secue ID cad); and/o Something You Ae a biometic. The apid gowth in the use of e-commece applications and penetation of infomation technology into daily life equies eliable use identification fo effective and secued access contol. A biometic system is essentially a patten ecognition system that opeates by acquiing biometic data fom an individual, extacting a featue set fom the acquied data, and compaing this featue set against the template set in the database. Authentication may be defined as Poviding the ight peson with the ight pivileges the ight access at the ight time. Chaacteistics of a biometic that must be pesent in ode to use the system fo authentication puposes ae Uniqueness - The same tait does not appea in two people. Univesality - The tait has to be pesent in many people as possible. Measuability - The tait can be measued with simple technical instuments. Use fiendliness - The tait can be easily acquied with minimal discomfot. Taditional pesonal authentication systems such as swipe cads, keys, and smat cads offe only limited secuity and ae uneliable. Fo example, cads o keys may be lost o stolen, and PINs may be known by unauthoized pesons. In ode to emedy secuity poblems inheent in taditional pesonal veification methods, biometic veification techniques have been intensively studied and developed to impove the eliability of pesonal veification. All biometic veification techniques deal with vaious human physiological featues including fingepints, hand geomety, handwitten signatues, etinal pattens, and facial images which ae all unimodal biometic authentication systems. A. Palm Dosal Biometics Hand-based biometics has eceived consideable attention ove ecent yeas which exploit seveal intenal and extenal featues that ae quite distinct in an individual. The shape of the subcutaneous vascula tee of back of hand contains infomation that is capable of authenticating the identity of an individual. Vein biometic systems ecod subcutaneous Infa Red absoption pattens to poduce unique and pivate identification templates fo uses. The use acceptance fo the hand-based biometics system is vey high, which ae becoming moe convenient and use fiendly. A elatively new biometic featue is the hand vein patten. The vascula patten netwok appeaing on the back of hand(palm dosa), efeed to as the hand vein in this pape, is extemely difficult to foge and, theefoe, offes pomising biometic which also ensues liveness. Thee ae many good popeties of this kind of biometic featue: 1) the vein infomation can epesent the liveness of an object; 2) it is difficult to be damaged and modified as an intenal featue; 3) it is difficult to simulated using a fake palm [2]. This poposed wok pesents a novel extaction method based on one of the newest biometic techniques which ae the vein pattens in the back of the hand, coupled with the knuckle featues. In the following section, the pinciple of palm vein

2 biometics and a bief eview on the elated pio wok is pesented. B. The Pinciple of Palm Vein Biometics Infaed (IR) is electomagnetic adiation whose wavelength is longe than that of visible light, and Infaed light has a ange of wavelengths lies between about 750nm and 1mm, just like visible light has wavelengths that ange fom ed light to violet. Infaed is commonly divided into 3 spectal egions: nea, mid and fa-infaed light, but the boundaies between them ae not ageed upon [14]. Vein pattens cannot be obseved using nomal, visible ays of light since they ae beneath the skin's suface. Thee ae two choices that focuses on imaging of vein pattens in hand by infaed light, the fa-infaed (FIR) imaging and the nea-infaed (NIR) imaging, which ae suitable to captue human bodies images in a non-hamful way [12]. In the FIR method, supeficial human veins have highe tempeatue than the suounding tissues. In the NIR way, the light of ( nm) wavelength is almost completely absobed by the deoxidized hemoglobin in vein while almost penetated the oxidized hemoglobin in the ateies. Nea IR imaging is moe toleant to changes in envionmental and body conditions. It can captue the majo vein pattens in the back of the hand as effectively as the FIR imaging technique. It poduces good quality images when captuing vein pattens in the back of the hand, palm, and wist. Theefoe, in the poposed wok, the existing nea IR image database of palm dosal suface is used. C. Existing system The uniqueness of hand vein pattens has attacted the attention of eseaches fo its usage in the pesonal identification and veification. Wang and Leedham [3] pesent an appoach fo pesonal authentication using hand vein images acquied fom the themal imaging. The Authos have descibed the methodology using the Hausdoff distance to geneate matching scoes between the extacted vein pattens and illustated pomising esults. Lin and Fan [4] have investigated the pesonal veification fom palm dosal images acquied fom the themal infaed (IR) camea opeating in m ange. This is a fully automated appoach and uses the combination of multi esolution epesentations fom the themal vein pattens that is being post pocessed. Fig. 1 The Exact Place of Hand Vein Extaction An individual's vein patten image is captued by adiating his/he hand with nea-infaed ays. The eflection method illuminates the palm using an infaed ay and captues the light given off by the egion afte diffusion though the palm. The deoxidized hemoglobin in the in the vein vessels absobs the infaed ay, theeby educing the eflection ate and causing the veins to appea as a black patten. This vein patten is then veified against a peegisteed patten to authenticate the individual. Coss and Smith [5] have detailed the usage of nea IR imaging fo the extaction of hand vein pattens. The Authos have demonstated the two-fold matching of medial axis epesentation, following the vein skeleton extaction. Tanaka and Kubo [6] also developed hand vein acquisition device using nea IR imaging and employed FFT based phase coelation scheme fo use veification. The themal (fa IR) imaging cameas ae highly sensitive to ambient conditions and vey expensive. Wheeas nea IR imaging is moe toleant to changes in envionmental and body conditions. It poduces good quality images when captuing vein pattens in the back of the hand, palm, and wist. Theefoe, in the poposed wok, the database of nea IR images is used. Shi Zhao, Yiding Wang and Yunhong Wang [7], poposed a biometic technique using hand-dosa, extacting vein stuctues. The poposed method makes using low-cost devices possible. The esults show that they could extact the vein netwoks as successfully as using high-quality images. A new method to acquie vein images, which could enhance the contast, is poposed, and the algoithm of extacting the vein patten fom low quality images is put fowad. They also poposed a novel denoising algoithm. D. Poposed System In this pape, we develop a new hand vein extaction and authentication appoach using minutiae matching algoithm. The advantages of the poposed system ae twofold. Fist, since the vein pattens lies as intenal featue in the human body, the theat of any damage to the vein pattens can be esticted and also povides bette accuacy. Second, a highe pefomance can be ensued due to the usage of bimodal featues, which can be acquied fom a single hand image without any inconvenience to the uses. The appoach has been adapted to utilize the existing database of palm dosal nea IR images. The block diagam of the poposed appoach is shown in Fig This pape investigates the extaction and matching of hand vein stuctue using the minutiae tiangulation. 2. This pape also investigates the utility of knuckle shape featues that can be simultaneously extacted fom the input hand vein images.

3 Fig 2. Block diagam of the poposed system Thee ae many diffeent biometic featues that ae elated to hand based biometics exist. They ae hand geomety, hand finge geomety, finge knuckle pint, fingepint etc. This poposed wok focuses on extaction of back of hand vein pattens using the binaization and thinning methods, thus paving way fo pefoming authentication. It also investigates the utility of knuckle featues fo image alignment and extaction of ROI, which futhe finds a way fo achieving bette accuacy. The image contous extacted fom the binaized input images ae used fo the image nomalization and segmentation of ROI which is detailed in Sections II IV. The extaction of hand vein map fom ROI images using thinning is descibed in Section V. The extaction and tiangulation of featue points fom the hand vein map is detailed in Sections VI and VII espectively. The matching schema fo the tiplets of minutiae is intoduced in Section VIII. The expeiments and esults fom this wok ae pesented in Section IX which is followed by the discussion in Section X and the main conclusions fom this pape ae summaized Section XI. II. IMAGE SEGMENTATION In image analysis, Thesholding is the simplest method of image segmentation. Individual pixels in a gayscale image ae maked as 'foegound' pixels if thei value is geate than some theshold value and as 'backgound' pixels othewise. Vein patten IR gey-scale images ae pocessed binaized and the extacted vein patten ae stoed within a elational database. The poposed system eceives the nea IR images of the palm dosa as input fom the existing database. Segmentation methods can be divided into fou goups which ae theshold-based segmentation, edge based segmentation, egion-based segmentation and segmentation by matching. The poposed system employs theshold-based segmentation. As a fist step in segmentation, the image is binaized using the OTSU thesholding Intenational Jounal of Advanced Engineeing Technology E-ISSN algoithm [8]. Otsu's method is used to automatically pefom histogam shape-based image thesholding o, the eduction of a gay level image to a binay image. The algoithm assumes that the image to be thesholded contains two classes of pixels (e.g. foegound and backgound) then calculates the optimum theshold sepaating those two classes so that thei combined spead (inta-class vaiance) is minimal. The binaized image is as shown in Fig. 5. III. KNUCKLE COORDINATES EXTRACTION One of the key tasks in image nomalization is to obtain eliable key points fom the contou of the binaized hand image. The contou of the input hand image is obtained by the edge detection method. The suitable edge detecto is used. The poposed system involves the canny edge detecto. The contou of the coesponding input hand image is as shown in Fig. 5. The obtained key points can be used fo vetical alignment of image and extaction of ROI. In the poposed system, the knuckle tips ae selected as the key contol points. Fig. 3. Input Image afte Thesholding and Coesponding Contou image The knuckle tips can be easily extacted by scanning the contou image fom left to ight. The point whee the fist tansition occus fom white to black pixel is the fist knuckle tip K. Simila scanning will yield the middle finge knuckle tip finge knuckle tip K. i K m and ing Fig. 4. Extaction of ROI fom Input Database Image IV. IMAGE NORMALIZATION AND REGION OF INTEREST EXTRACTION K, K, K ae Once the key contol points ( ) located, the input hand vein image is fist oiented to extact the eliable egion of inteest. The angle ω, which is the angle between the line joining Ki and K m and the line joining K i and i m K is fistly computed using the fomula as shown below. The otation matix Ω is used to achieve the vetical alignment of the egion of inteest, fom each of the input palm dosal images, as follows: cos( ω) sin( ω) ( ) ( ) Ω= sinω cosω ω = tan 1 K K ( y) Ki( y) ( x) K ( x) i

4 whee ( x, y) epesents the coesponding coodinates of knuckle tip points. The egion of inteest is extacted by some pixels below the middle knuckle tip K m. The K m and K ae selected as left and ight boundaies of the egion of inteest. The extacted egion of inteest is of some fixed size only [8]. V. VEIN MAP THINNING The topological stuctue of vein pattens is extacted fom the segmented egion of inteest. The flow diagam fo extacting the vein patten is shown in fig 7. Fist, the images ae subjected to adaptive histogam equalization fo image enhancement using local gay level infomation. Adaptive histogam equalization is a compute image pocessing technique used to impove contast in images. It diffes fom odinay histogam equalization in the espect that the adaptive method computes seveal histogams, each coesponding to a distinct section of the image, and uses them to edistibute the lightness values of the image. Fig 5. ROI afte median filteing The enhanced images ae futhe filteed using the median filteing method. Median filteing is one kind of smoothing technique. All smoothing techniques ae effective at emoving noise in smooth patches o smooth egions of a signal, but advesely affect edges. Edges ae of citical impotance to the visual appeaance of images. The median filte is a simple edgepeseving smoothing filte. The esulting image is subjected to thinning which geneates the vein patten stuctue. The Region of Inteest is pocessed and filteed using median filteing. The esulting image is shown in Fig. 5. Fig 6. Extacted hand vein topology Thinning is a mophological opeation that is used to emove selected foegound pixels fom binay images, somewhat like eosion o opening. It can be used fo seveal applications, but is paticulaly useful fo skeletonization [11]. In this mode it is commonly used to tidy up the output of edge detectos by educing all lines to single pixel thickness. Thinning is nomally only applied to binay images, and poduces anothe binay image as output. Fig 7. Extaction of Vein Patten fom the Palm Dosal ROI images VI. EXTRACTION OF MINUTIAE POINTS Afte the extaction of the vein stuctue, the next step is to extact the minutiae points. The individuality of vein stuctue is exclusively detemined fom the elationship among the local vein chaacteistics. Theefoe, the extacted vein patten is fistly used to locate the key points that ae elatively stable, unique and epeatable. The vein bifucation and endings points ae selected as key points to extact local vein popeties. A vein bifucation is defined as vein point whee vein foks o diveges into banch veins, and the vein ending is the point at which vein ends o disappeas abuptly. The algoithm used fo extacting these minutiae points is the cossing numbe (CN) algoithm. Cossing Numbe Algoithm This method extacts the vein endings and bifucations fom the skeleton image by examining the local neighbohood of each idge pixel using a 3 3 window. In ode to extact the vein endings and bifucation points we examining the connectivity of evey pixel and detemine the cossing numbe [9] fo evey pixel. This method involves the use of the skeleton image whee the idge flow patten is eightconnected. The minutiae ae extacted by scanning the local neighbohood of each idge pixel in the image using a 3 3 window. CN is defined as half the sum of the diffeences between the pais of adjacent pixel [24]. Fo a pixel q, the eight pixels ae scanned in an anticlockwise diection. The pixel can be classified afte obtaining its pixel value. The coodinates of the idge segment and type of minutiae of each minutiae point is ecoded fo each minutiae. Afte a successful extaction of minutiae, they ae stoed in a template, which may contain the minutia position (x, y). Duing the enollment the extacted template ae stoed in the database and will be used in the matching pocess as efeence template o database template. Duing the veification o identification, the extacted minutiae that ae stoed in a database and ae used as quey template duing the matching. If the cental pixel is 1 and has exactly 3 one-value neighbos, then the cental pixel is a vein bifucation. If the cental pixel is 1 and has only 1 one-value neighbo, then the cental pixel is a vein ending. VII. TRIPLETS FORMATION USING MINUTIAE TRIANGULATION METHOD The featue extaction appoach is to use unique topological stuctue fom the hand vein minutiae

5 using Delaunay tiangulation. Using the extacted featues, the hand vein map of one peson is uniquely distinguished fom that of the othe peson. In othe wods, the pocess of authenticating a ight peson is done based on the tiplets that ae extacted using the Delaunay tiangulation method. Delaunay Tiangulation Method Delaunay tiangulation can divide a suface into egions that ae paticulaly well-suited fo image pocessing applications. Given a set of points P, the Delaunay tiangulation of this set ensues that no point is in the cicumcicle of any tiangle fomed. The vetex of a tiangle coesponds to a minutiae point, and the edge of the tiangle joins togethe two neighboing minutiae points. Given a set S of points p1, p2,..., PN, we can compute the Delaunay tiangulation of S by fist computing its Voonoi diagam. The Voonoi diagam decomposes the 2D space into egions aound each point such that all the points in the egion aound pi ae close to pi than they ae to any othe point in S. Given the Voonoi diagam, the Delaunay tiangulation can be fomed by connecting the centes of evey pai of neighboing Voonoi egions. Ou algoithm gouped all the minutiae into tiplets of minutiae. Fo each of these tiplets of minutiae, we stoed the distance of one of the minutiae fom both othe minutiae. The idea is to extact meaningful minutiae goups, i.e., tiplets o tiangles, fom the hand vein map to achieve otation and tanslation invaiant epesentation of local infomation as shown in fig 9. database image. In the evese case, the scoes geneated by subtacting the bifucation tiplets count of the database image fom the bifucation tiplets count of the input image. The absolute value of the scoes is updated fo each and evey input image. The same pocedue is epeated fo geneating scoes in case of end points. Now the scoe values ae geneated fo both the bifucation and end point tiplets sepaately (Fig. 10 and Fig. 11). Fig 10.Bifucation points and its coesponding tiplets geneated The authentication is done based on these two scoe values. Now in the ecognition phase, the scoe geneated by the bifucation tiplets is stoed in one vaiable and the scoe geneated by the bifucation tiplets is stoed in anothe vaiable. The minimum values out of both the values ae calculated. Now the index values fo which the scoe value is minimum is identified. The coesponding database image fo which the index value is minimum is chosen and it is etieved as the matched image. Fig 9. A tiplet fomed using minutiae points Similaly the possible numbes of tiangles o tiplets fo the extacted minutiae points ae calculated using the Delaunay method in MATLAB. In MATLAB, the method is executed using the DelaunayTi function. The total no of tiplets fomed is stoed sepaately fo both the bifucation and ending points using two diffeent vaiables. The authentication is done based on the no of tiplets that ae fomed sepaately fo bifucation and ending points (Fig 10 and 11). VIII. SCORE GENERATION AND AUTHENTICATION The scoes ae geneated by subtacting the count of bifucation tiplets of the input image fom the count of bifucation tiplets of the database image, povided a condition fo checking whethe the count of minutiae tiangles fo database image is bigge. Fo subtaction pupose the size of the input image is padded with zeos if its size is less than the Fig 11. End points and its coesponding tiplets geneated IX. CONCLUSION AND FUTURE WORK Palm vein pattens ae invisible and vitually impossible to foge, making the system highly secue. The digitally encypted palm vein pattens cannot be ead by any othe system. The test take need not want to touch the palm vein senso, eliminating the possibility of smudging. The system is also much moe accessible fo people with some physical disabilities. These factos act as the motivation fo this poposed poject. The notable advantages of the poposed hand- based authentication system ae Non-contact ecognition has no bad effect on public health. Incease the fogey difficulty by using the invisible featues inside the human body, which only appeas unde infaed light. Do not need to conside the skin suface and can pevent the atificial finges.

6 Uniqueness, stability and stong immunity to fogey. Identical twins have diffeent and distinct vascula pattens. Vein pattens ae not easily spoofed, obseved, damaged, obscued o changed. FUTURE WORK Multimodal fusion biometics ecognition is a main steam tend in the futue. In futue, the vein tait is able to conjunct with othe biometics to fom a multi-modal ecognition system. Diffeent biometic featues have diffeent chaactes inheently which can be combined along with the vein tait. Multimodal fusion povides an oppotunity to employ the advantages of such diffeent biometic featues such as cease textue, palmpint featue, knuckle pint featue, hand geomety, finge geomety, fingepint etc. REFERENCES 1. Anil K. Jain, Fellow, IEEE, Aun Ross, Membe, IEEE, and Salil Pabhaka, Membe, IEEE An Intoduction to Biometic Recognition, IEEE tansactions on cicuits and systems fo video technology, vol. 14, no. 1, Januay Yi-Bo Zhang, Qin Li, Jane You, and Pabi Bhattachaya, Palm Vein Extaction and Matching fo Pesonal Authentication, VISUAL 2007, LNCS 4781, pp , L. Wang and G. Leedham, A themal handvein patten veification system, in Patten Recognition and Image Analysis, S. Singh, M. Singh, C. Apte, and P. Pene, Eds. New Yok: Spinge, 2005, vol. 3687, pp C.-L. Lin and K.-C. Fan, Biometic veification using themal images of palmdosa vein pattens, IEEE Tans. Cicuits Syst. Video Technol., vol. 14, no. 2, pp , Feb J. M. Coss and C. L. Smith, Themo gaphic imaging of the subcutaneous vascula netwok of the back of the hand fo biometic identification, in Poc. IEEE 29th Annu. Int. Canahan Conf. Secuity Technology, Sande-Stead, Suey, U.K., Oct. 1995, pp T. Tanaka and N. Kubo, Biometic authentication by hand vein pattens, in Poc. SICE Annu. Conf., Yokohama, Japan, Aug. 2004, pp S. Zhao, Y. Wang, and Y. Wang, Biometic veification by extacting hand vein pattens fom low-quality images, in Poc. 4th ICIG, Aug. 2007, pp A. Kuma and K. V. Pathyusha, Pesonal authentication using hand vein tiangulation, in Poc. SPIE Biometic Technology fo Human Identification, Olando, FL, Ma. 2008, vol. 6944, p E. 9. R.C Gonzalez, R.E Woods Digital Image Pocessing using MATLAB, Pentice Hall, 1st Edition, J. Mehnet, J. M. Coss, and C. L. Smith, Themal gaphic imaging: Segmentation of the subcutaneous vascula netwok of the back of the hand, Reseach Rep., Edith Cowan Univ., Austalian Inst. Secuity Appl. Technol., Peth, Westen Austalia, Wang Kejun, Xiong Xinyan, Ren Zhen, Fu Bin, Gay-Scale Skeletonization of Neainfaed Vein Pattens Using the Impoved Wateshed Algoithm in Vein Patten Biometics, Industial Electonics and Applications, ICIEA 2009, L. Wang, G. Leedham and S.-Y. Cho, Infaed imaging of hand vein pattens fo biometic Puposes, IET Comput. Vis., 2007, 1, (3 4), pp Ajay Kuma, Senio Membe, IEEE, and Ch. Ravikanth, Pesonal Authentication Using Finge Knuckle Suface, IEEE tansactions on infomation foensics and secuity, vol. 4, no. 1, mach L.Wang and G. Leedham, Nea- and fainfaed imaging fo vein patten biometics, in Poc. IEEE Int. Conf. Video Signal Based Suveillance, Sydney, Nov. 2006, pp S. K. Im, H. M. Pak, Y. W. Kim, S. C. Han, S. W. Kim, and C. H. Hang, An biometic identification system by extacting hand vein pattens, J. Koean Phys. Soc., vol. 38, pp , Ma