Iris Recognition Using Semantic Indexing

Transcription

1 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P Irs Recognton Usng Semantc Indexng Lath A. Al-An *, Mohammed Sahb Altae, Ansam Ahmed Alwan Deartment of Physcs, College of Scence, Unversty of Al-Nahran, Baghdad, Iraq Deartment of Comuters, College of Scence, Unversty of Al-Nahran, Baghdad, Iraq Abstract The rs of human eye s one of the most useful trats for bometrc recognton. Ths aer resents an rs recognton system based on semantc ndexng. The roosed system uses the concets of latent semantc ndexng (LSI) for rs recognton. One technque of LSI s the sngular value decomoston (SVD). The SVD s an nformaton retreval uses numercal decomoston methods to comute one characterstc value (.e. SVD) for each rs mage to be used as a recognton feature. The roosed system conssts of two hases: the tranng and recognton. The tranng hase s resonsble on storng the rs models n the database, whle the task of recognton hase s to comute the smlarty measure between the SVD of the query rs mage and SVDs of the rs mages found n the database. The recognton decson s made accordng to the normalzed smlartes and aeared as a text message tells what the dentty t s. The successful recognton rate was about 96%, whch ensure the successful of the emloyed method and correct ath of comutatons. * mohamedaltae@yahoo.com.. (LSI). SVD.LSI (SVD). (SVD ),. : SVD. SVD.. %96 37

2 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P Introducton Irs scan has been develong a recognton system caable of ostvely dentfyng and verfyng the dentty of ndvduals wthout hyscal contact or human nterventon, such technology shows romse of overcomng revous shortcomngs []. Image rocessng technques can be emloyed to extract the unque rs attern from a dgtzed mage of the eye, and encode t nto a bometrc temlate, whch can be stored n a database. Ths bometrc temlate contans an objectve mathematcal reresentaton of the unque nformaton stored n the rs, and allows comarsons to be made between temlates []. Dougman s oneer how nvestgate the ablty of the rs to verfy the dentty. He resented a method for rad vsual recognton of ersonal dentty based on the falure of statstcal test of ndeendence. The vsble texture of erson's rs n a real-tme vdeo mage s encoded nto a sequence of mult-scale quadrature D Gabor wavelet coeffcents, whose most-sgnfcant bts comrse a 56-byte "rs code" [3]. Daouk used Canny edge detecton scheme and a crcular Hough transform to detect the rs boundares n the eye s dgtal mage. They then aled the Haar wavelet n order to extract the determnstc atterns n a erson s rs n the form of a feature vector [4]. Masek resented an rs recognton method, the Log-Gabor flter was extracted and quantzed to four levels to encode the unque attern of the rs nto a btwse bometrc temlate. The Hammng dstance was emloyed for classfcaton of rs temlates [5]. Dong suggested some restrctng factors of rs mage acquston, whch were analyzed and the otcal formulas were derved. They roosed two novel rs recognton systems wth good human-comuter-nterface but wth two dfferent strateges whch resectvely meet the requrements of low-end and hgh-end market. [6]. Sudha resented that rs recognton s a otental tool n secure ersonal dentfcaton and authentcaton n vew of roertes such as unqueness, a new aroach based on the Hausdorff dstance measure s roosed for rs recognton [7]. Ths aer resents newly roosed relable method for rs recognton roblem; such method uses the semantc ndexng technque to establsh erson recognton system. Semantc features (SVD and U) were used to dentfy the 38 enrolled rses, these feature were stored n the system to be used later n the recognton rocess. More detals about the roosed method are exlaned n the followng sectons:. Proosed Irs Recognton Method The used aroach s a numercal method deends on fndng set of solutons usng set of nut data; t nut set of rs mages. Each rs mage ass through mult-stages, the frst s rs localzaton stage ams at extractng just the rs segment from the mage. Ths s carred out by newly suggested method, whch determnes the dameter and center of the rs. Next stage s rs documentaton whch ncludes encodng and then ndexng the extracted rs. The generc structure of the roosed rs recognton method s shown n Fgure (). It s shown the roosed method s desgned to be conssted of two hases: the tranng and recognton. Enrollment Store n Codebook Yes End Load Irs Image Irs Detecton Irs Maskng Irs Encodng Code Indexng Phase Another Test No Recognton Irs Localzaton Irs Documentaton Code Mang SVD Comutng Smlarty Measurng Recognton Decson Fgure - Block Dagram Shows The Proosed Irs Recognton Method. The tranng s an offlne hase n whch the test rses are stored n a database fle. Whereas the recognton s an onlne hase ncludes: rs mang whch attemt to estmate the semantc features SVD and U for all test mages (.e.

3 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P query and those encoded n the database). Last stage s a comarson based on semantc features between the query rs and that found n the database, the result of the comarson wll determne the smlarty measure between the consdered rses and then hel to make the recognton decson. In the followng more exlanaton about each stage are gven n detals.. Irs Localzaton It s a rerocessng stage, n whch the rs s extracted from the eye mage. The roosed method requres assng through three stes: Irs determnaton, ul determnaton, and rs maskng. A- Irs Determnaton The sclera aears at relatvely more exanded brght regon than other cues n the eye; t s shown as great as close the rs. The horzontal brghtness dstrbuton (HBD) of the eye mage can be comuted, HBD s a vector has a long equal to the wdth of the mage, each value n such vector reresent the sum of all the xel values that belongng to ts corresondng column n the mage. h = y= 0 HBD x f xy () Where, f s the xel values of the mage, x and y are ndces referrng to the oston of the current xel. The behavor of HBD s found contans two greater characterstc eaks restrctng lower valley n between as Fgure (3) shows. lowest valley, one can determne the dameter (D) and central horzontal oston of the rs () (x o ) as follows: D= x x (3) x + x x o = One can use a vertcal search wth crcular wndow of dameter D along the vertcal lne of the oston xo to fnd darkest regon (less average) enclosed by the wndow. The determned darkest crcular wndow s the detected rs regon, and the oston of the center of the determned wndow s the oston (y o ) of the center of the rs on the vertcal coordnate. B- Pul Detecton The ul detecton ams to determne just the regon of the ul nsde the rs, the adoted method deends on the theory of the otmal search [8]. By assumng the ntal search ont s the center of the rs (x o,y o ), one can determne the average of all xel values ncluded n a crcle centered at (x o,y o ) wth ntal radus of R = to be the central crcle. Also, the averages (A u, A down, A left, and A rght ) of all xel values belongng to the four crcles of radus 3 xels and centers are shfted by xel u and down the center of the central crcle are comuted as shown n Fgure (4). It s notced that the four termnal crcles are nterfered wth the central one, snce they lng on the crcumference of the central crcle. Fgure - Human Eye Desgn. Fgure 4- The Four Crcles Around The Central One Of The Pul Detecton. Fgure 3- The HBD Behavor. The two characterstc eaks refer to the exanded brght regons of sclera on the both sdes of rs, whle the valley refers to the dark regon of the rs n the mage. By smoothng the behavor of the HBD and searchng the locaton (x and x ) of the greatest two eaks enclose the 39 By comarng A u wth A down, the central crcle s shfted toward the crcle of the less average by a vertcal amount s xel. Also by the same manner, the comarson between A left and A rght determnes the drecton of the next horzontal shft. In case of equalng each two averages on the corresondng termnals, the radus of the central crcle s ncreased by one and then the comarson of the new four crcles s reeated. The comarson and the exanson of the central

4 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P crcle are contnued untl reachng a termnaton condton, whch necesstate exstng at least three averages are greater than a secfc threshold (T). In such case the central crcle s fttng the sze of the ul aears n the rs mage. The threshold value may equal any assumed color value that belong to the rs not ul. C- Irs Maskng In the rs maskng ste, the re-determned dameter and oston of the center of rs n the mage are emloyed to construct a mask M; whch s a -D array takes the same sze of the eye mage.e..m(w,h). The mask M has bnary values: t takes at the regon belongng to the rs, otherwse t takes a zero values accordng to the followng condton: f (x-xo) + (y-yo) D/ M(x, y) = (4) 0 Otherwse In order to extract just the rs from the eye mage, one can check f the mask value M(x,y) corresondng to the oston of the current xel f(x,y) s or 0. Accordng to the check result, the current xel s acceted as rs or not as follows: f M(x, y) = then f(x, y) s a xel belong to the rs Otherwse leave the current xel Fgure 5- The Irs Mask.. Irs Documentaton Ths stage nvolves encodng of the rs mage though resentaton of trustworthy document of rs code to corroborate the dentty of a trusted ndvdual n the enrollment or recognton stage. Durng the documentaton, a number of rs samles are encoded and then ndexed as follows: A- Irs Transformaton The Cartesan to olar reference transform should be authorzed to be equvalent rectangular reresentaton of the zone of nterest as shown Fgure (6). In ths way, the stretchng of the rs texture s comensated as the ul changes n sze, the frequency nformaton contaned n the crcular texture s unfolded n order to facltate next features extracton. 40 Moreover ths new reresentaton of the rs breaks the eccentrcty of the rs and the ul. The h arameter and dmensonless q arameter descrbe the olar coordnate system. Thus the followng equatons mlement the transformaton of the mage f(x(r,q),y(r,q )) from the Cartesan to olar coordnates: x( ρ, θ ) = ( ρ) x y( ρ, θ ) = ( ρ ) y and, x ( θ ) = x ( θ ) + R y ( θ ) = y ( θ ) + R Fgure 6- Irs Rectangular Reresentaton. o o ( θ ) + ρ x ( θ ) ( θ ) + ρ y ( θ ) cos( θ ) sn( θ ) x ( θ ) = xo ( θ ) + R cos( θ ) (7) y ( θ ) = yo( θ ) + R sn( θ ) Where R and R are resectvely the radus of the ul and the rs, whle (x (h),y (h)) and (x (h),y (h)) are the coordnates of the ullary and lmbc boundares n the drecton h. The fronter zones (rs/ul and rs/sclera) are truncated to avod nosng the rs rectangular reresentaton by other atterns not ncluded n the rs texture. It s notced that () the ul s not erfectly crcular, and () the outer rs boundary detecton s often not well defned due to the ostonng of contact lens bound. B- Irs Encodng To encode the rs mage, the texture of the rectangular reresentaton of the rs s bnarzed usng a dynamc threshold drven from the hstogram of the rs mage. The dstrbuton of the hstogram s normal smlar to bell shae,.e. Gaussan dstrbuton. The threshold takes the color corresondng to the maxmum value found n the hstogram. The result wll be a bnary texture look lke the bar code as shown n Fgure (7). Fgure 7- Irs Encodng. (5) (6)

5 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P C- Irs Code Indexng The rocedure of the ndexng s carred out by arrangng the rs codes as a sequence of samles, see Fgure (8). Then, the coded rs can be understood as a vector of an m-dmensonal sace, where m denotes the number of xels (attrbutes) found n the rs mage. Let the symbol A denotes m n term-document matrx related to m terms (rs codes) n n documents (mage samles). So that, the term-document matrx A s reresentng a sequence of coded mages, n whch the element (,j) reresent the xel value of th term n the j th samle document. Irs mage Fgure 8- Irs Indexng. Term-document The nterest ont n such arrangement s the document matrx. The term-document matrx s caable to be udated each tme when new rs code s ndexed. An addtonal two arrays are made to save the number of documents (NoDoc) and number of terms at each document (NoTrm). 3. Tranng and Recognton The enrollment and recognton hases are two oeratng modes desgned for the roosed rs recognton to be work wthn. The enrollment ncludes the frst stages of; rs localzaton and rs documentaton. The addtonal dstnct stage n the enrollment hase s savng the termdocuments matrx n a database aend fle called codebook. Because the adoted aroach s numercal method, t needs several samles of rs mages to be work. Such that, the database fle should be ncludes the revously encoded samles of the rses belong to dfferent ersons. In addton, an ndeendent two fles are made; the frst s emloyed two store the number of documents, and the later s aend one used to store the numbers of terms sequentally n each document. Whereas, the recognton hase works only when the database fle s contans enough number of rs samles through the enrollment. (.e. greater than rs). The query mage of the rs wants to be recognzed are nut to the roosed rs recognton system that work through the recognton mode. The query 4 rs wth database rses s collected to mlement the recognton task. Also, the recognton hase s comosed of two dstnct stages n addton to the frst ones. The dstnct stages are secfed for makng the recognton decson; these dstnct stages of the recognton hase are exlaned n the followng: 3. Irs Code Mang Egen roblem s emloyed as a tool to ma the rs code nto latent semantc features n order to solve the rs recognton roblem. The egenroblem uses the numercal lnear algebra as a bass for nformaton retreval. Many numercal methods are used n ths urose, ower method s adoted n the resent work to estmate the egenvalue and egenvector snce t regarded as the most common and tranng one. The egenroblem based features mang nvolves egenvalue and egenvector of A, whch s stll very memory and tme consumng oeraton. The use of just the rs bnary code nstead of the whole mage n the ndexng leads to reduce the comutaton tme and memory sze, such that the oeraton beng faster even at large data collecton. The mang of the nonsquare matrx A by the egenroblem of A T A (where T refers to the transose suerscrt) usng ower method can be obtaned very effectvely. The features of the ndexed rs code n the A are maed nto new semantc ones are: the SVD whch s the square root of the egenvalue, and the uer vector U assocated to the meant SVD that equal to the egenvector corresondng to the same egenvalue. Both the SVD and U are comuted for each document n A and stored n a looku table as Fgure (9) shows. Code of test samles Code of query samle Looku table SVD U vector Fgure 9- Mang The Term-Document Matrx Into Semantc Features. The semantc features; SVD and U are comuted for the codes of query rs code and all test samles (rs codes) found n the codebook. The document n A are regarded as a

6 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P comarable encoded classes. The comuted semantc features (SVD and U) are sequentally arranged n looku table. The looku table contans the SVDs of all rs samles found n the frst column followed by U for each samle. Looku table s effectvely smlfed the recognton score comutaton. The SVD s a sngle numercal feature that can rovde a quanttatve assgnment for the query rs to the numercally closest class. Whle the U s a set of onts (curve) can ctures the behavor of each rs samle, and also t rovdes a qualtatve ndcaton about the closest class may be found n the codebook. 3. Recognton Decson Makng The smlarty measure between any two samles s made to be ercent value related to the amount of the mean squared dfference of the semantc features belong to the query rs and the database of the encoded samles, as gven n the followng relatonsh: S = w w ( SVD SVD q) N S ( SVD k SVD q) k = N S ( U j U q) = N S N S ( U kj U q) k j = j= +... (7) (8) here, s onter refers to the current samle n the codebook, k s onter refers to each samle n the codebook, j s onter refers to the seral of current value of the U, N v s the number of values n the vector U, N S s the number of rs samles found n the database, whle w and w are the contrbuton weghts of the SVD and U resectvely n makng the recognton decson, the default values of them are 0.5 for each. These weghts can be vared through out the analyss, esecally for studyng ther effect on the recognton score. Fgure (0) shows the rocedure of comutng the smlarty ercentages schematcally accordng to the comarson between the semantc features of the query rs and those found n the database. Ths oeraton ams to normalze the smlartes (S ) resultng from the comarson. The normalzaton s curred out by dvdng each S by the summaton of all the smlartes as follows: NS = N s S j= S j 4. Recognton Results The results of the roosed rs recognton based on semantc features were good ssues. The smlartes between the query rs samle and those found n the codebook were comuted as ercent measure. The recognton decson was made referrng to the rs samle n the codebook that ossesses hgher smlarty ercent. Fgures ( and ) show the SVD values and U vector behavors of rs samles n the codebook, whle Fgure (3) shows the recognton results between all avalable query rses wth that of the codebook, these results save the chance to make quanttatve and qualtatve analyss to evaluate the erformance of the roosed rs recognton based on semantc features. SVD U Vector Rank Fgure - Resulted SVD Values. Samle () Samle () Samle (3) Samle (4) Samle (5) Samle (6) Samle (7) Samle (8) Samle (9) Samle (0) Fgure - Resulted U vectors. (9) Samle () Samle () Samle (3) Samle (4) Samle (5) Samle (6) Samle (7) Samle (8) Samle (9) Samle (0) 00 Fgure 0- Smlarty Matchng Between The Query And Ten Looku Table Irses. Recognton Percent % Samle () Samle () Samle (3) Samle (4) Samle (5) Samle (6) Samle (7) Samle (8) Samle (9) Samle (0) Fgure 3- Recognton Results. 4

7 Al-An et.al. Iraq Journal of Scence, December 0, Vol. 53, No. 4, P Results And Dscusson By notng the SVD values n Fgure (), t s found that the SVD values are fractons n between 0-. Some of them may dffers from each other at (or after) the second decmal order, whch ndcates the ablty of the SVD to recognze the rs mages. In corresondence, the U curves showed monotonc behavors for the dfferent rs samles n the codebook. It s clearly aeared that the U curves are aroachly dentfed at all ther onts excet the second one. Ths means the dfferences between U curves that comuted accordng to eq.(8) are greatly deendng on the second ont n each U curve,.e. the dfference s comuted by just one ont not all onts of the curve. Through out the analyss, an addtonal exerment to re-comute the recognton scores twce: ones by usng just the SVD (.e. the frst term of eq.8), and the second by usng the U only (.e. the second term of eq.8). The recognton results of such exerment were nearly dentfed n both cases, whch refers to the behavoral weakness of the U for rs recognton. Therefore, the recognton result usng just SVD s equvalent to the use of U only. Any one of SVD and U does not strength or weak the other. Such that, t can neglect the U n the recognton comutatons by drong the second term n eq.(8). The analytcal consderaton of SVD values n Fgure () shows that the dfferences between the SVD values were not equal whch greatly affect the recognton results. Snce the SVD became the unque resonsble arameter on the recognton, the results should be mroved by makng the centrodes of the SVD models n the codebook to be more dstngushed. The results mrovement s a new modfed SVD feature,.e. nstead of SVD, new feature based on SVD exonental fttng can be adoted. Ths feature s 00 SVD rather than SVD. Ths feature modfcaton made the centrodes to move away from each other and suort the recognton results. Fgure (4) shows the recognton results usng the modfed feature, t s shown that the average recognton ercent became 96%, whereas t was 84% by usng SVD. Ths enhancement n the recognton ensures the embeddng ablty of the SVD n the recognzng tasks. Fgure 4- Recognton Results Usng The Modfed Method. 6. Conclusons The use of semantc features was successful n recognzng the rs mages. The recognton result usng just SVD s equvalent to the use of U only. Any one of SVD or U does not strength or weak the another. The use of U s not necessary when usng SVD for the recognton uroses. Snce the SVD values are fractons, the classes of dfferent rses are found nterfered slghtly wth each other. Such that, the use of the modfed exonental feature as a functon of SVD was found more descrtve than just SVD. 7. References [] James, R., 005. "Irs recognton hotons to dentty", IEEE, 5, 3, -0. [] Encarnação, P., Veloso, A., 009. "bosgnals 009- nternatonal conference on bo-nsred systems and sgnal rocessng", Conference of Techncal Co- Sonsorsh by IEEE-EMB and IEEE-CAS, USA, January 4 7,. 93. [3] Gman, J. G., 993. "Hgh confdence vsual recognton of ersons by a test of statstcal ndeendence", IEEE, 5,, [4] Daouk, C. H., El-esber, L. A., Kammoun, F. D. and Alaou, M. A., 00 "Irs recognton", IEEE,, 5, [5] Masek, L., 003. "Recognton of human rs atterns for bometrc dentfcaton", M.SC. thess, unversty of western, Australa,. 53. [6] Dong, W., Sun, Z., Tan, T., 008. "How to make rs recognton easer", IEEE 3, 7, [7] Sudha, N., Puhan, N.B., Xa, H. and Jang, X., 009. "Irs recognton on edge mas", Journal of IET comuter. VIS., 3,, 7. [8] Salamon D "Data Comresson: the comlete reference", nd edton, Srnger ublcatons comany, USA,