Principal Components Analysis Based Iris Recognition and Identification System

Transcription

1 Iteratioal Joural of Soft Computig ad Egieerig (IJSCE) ISSN: , Volume-3, Issue-2, May 203 Pricipal Compoets Aalysis Based Iris Recogitio ad Idetificatio System E. Mattar Abstract his article focuses o the employmet of iris recogitio techique ad their applicatio i security systems. he implemetatio of such a system is based o the processig of a iris (scee) usig Priciple Compoet Aalysis ow i the literature as (PCA). his is doe by a iris segmetatio algorithm of (Libor Mase). Libor Mase algorithm is utilized here to segmet a iris from some udesired oises ad igrediets i a eye image. his rather helps to acquire the most accurate iris scee. Eige irises are hece obtaied usig the PCA method. Eige of irises are the utilized to trai a Artificial Neural Networ (ANN) recogitio system. his is followed by trasformig a set of irises ito a ew space. rasformed irises are accumulated i a database, where they are compared with a set of test irises trasformed i the same state of the recogitio cycle. he proposed system has resulted i accurate results up to 9 % for idetifyig a pre-stored idividuals. Idex erms Iris recogitio, Pricipal Compoet Aalysis, Patter recogitio, Eige vectors. I. INRODUCION A. he Biometric echology he use of biometric systems has bee icreasigly ecouraged by both govermet ad private etities i order to replace or improve traditioal security systems. he iris is commoly recogised as oe of the most reliable biometric measures: it has a radom morphogeesis ad o geetic peetrace. I 987, Flom ad Safir [] studied the problem ad cocluded that iris morphology remais stable throughout huma life, where they have estimated that, the probability of the existece of two similar irises o distict persos at i (072). A biometric system provides automatic recogitio of a idividual based o some uique feature or characteristic possessed by the idividual. Biometric systems have bee developed based o figerprits, facial features, voice, had geometry, hadwritig, the retia [2], ad the oe preseted i this paper, the iris. Biometric systems wor by first capturig a sample of the feature, such as recordig a digital soud sigal for voice recogitio, or taig a digital colour image for face recogitio. he sample is the trasformed usig some sort of mathematical fuctio i to a biometric template. ca the be objectively compared with other templates i order to determie idetity. Most biometric systems do permit two modes of operatio. A erolmet mode for addig templates to a database, ad a idetificatio mode, where a template is created for a idividual ad the a match is searched for i the database of pre-erolled templates [4]. Biometric systems wor by first capturig samples of the feature, such as image for face recogitio, digital voice for voice recogitio etc... Such samples are the trasformed usig some sort of mathematical fuctios to produce some characteristic property for idetificatio. A good biometric system is characterized by the use of a feature that is; highly uique so that the chace of ay two people havig the same characteristic will be miimal, stable so that the feature does ot chage over time, ad be easily captured i order to provide coveiece to the user, ad prevet misrepresetatio of the feature. B. he Huma Eye A iris is a thi circular diaphragm, which lies betwee the corea ad the les of the huma eye. A frot-o view of the iris is show i Fig... he iris is perforated close to its ceter by a circular aperture ow as the pupil. he fuctio of the iris is to cotrol the amout of light eterig through the pupil, ad this is doe by the sphicter ad the dilator muscles, which adjust the size of the pupil. he average diameter of the iris is 2 mm, ad the pupil size ca vary from % 80 % of the iris diameter [4]. A frot-o view of 0 to the iris is show i Fig. (). he iris is perforated close to its cetre by a circular aperture ow as the pupil. he fuctio of the iris is to cotrol the amout of light eterig through the pupil, ad this is doe by the sphicter ad the dilator muscles, which adjust the size of the pupil. he commo diameter of the iris is 2 mm, ad the pupil size ca vary from (0% to 80%) of the iris diameter [4]. he biometric template will provide a ormalized, efficiet ad highly discrimiatig represetatio of the feature, which Mauscript received May, 203. E. Mattar, Departmet of Electrical ad Electroics Egieerig, College of Egieerig, Uiversity of Bahrai, P. O. Box 384, Kigdom of Bahrai Fig. () : A frot view of the huma eye. Wolff, [4]. Formatio of iris begis durig the third moth of embryoic life [4]. he uique model o the surface of the iris is formed durig the first year of life, ad pigmetatio of 430

2 Pricipal Compoets Aalysis Based Iris Recogitio ad Idetificatio System the stroma taes place for the first few years. Formatio of the uique patters of the iris is radom ad ot related to ay geetic factors [4]. he oly characteristic that is depedet o geetics is the pigmetatio of the iris, which determies its color. Due to the epigeetic ature of iris patters, the two eyes of a idividual cotai completely idepedet iris patters, ad idetical twis possess ucorrelated iris patters. For further details o the aatomy of the huma eye cosult the boo by Wolff [4]. he iris had bee historically recogized to possess characteristics uique to each idividual. I the mid-980s, two ophthalmologists, (Leoard Flom ad Ara Safir) have proposed the cocept that o two irises are alie. (Leoard Flom ad Ara Safir), researched ad documeted the potetial of usig the iris for idetifyig people ad were awarded a patet i 987. Soo after, the itricate ad sophisticated algorithm that brought the cocept to reality was developed by (Joh Daugma, [4]) ad pateted i 994. he origial wor ad the cotiued developmet have established the Iridia s iris recogitio algorithm, [7] as the mathematically urivalled meas for autheticatio. Compared with other biometric techologies, such as face, speech ad figer recogitio, iris recogitio ca easily be cosidered as the most reliable form of biometric techology [4]. C. Research Objectives ad Outlie I this article a iris recogitio system based o Pricipal Compoet Aalysis (PCA) is implemeted. PCA is a method used to process a variety of data, where is well ow as a method that extract global ad fie features, respectively. I this research, it was adapted the ANN PCA related methodology for a Iris Recogitio. he developmet tool was doe through Matlab, where a emphasis was give oly to the software for performig recogitio, ad ot o the hardware for capturig a eye image. he performace of the ecodig techique was executed usig the (CASIA) eye images: with a courtesy from the Chiese Academy of Scieces Istitute of Automatio, (CASIA) [6]. Hece, followig will be the followed steps :. A algorithm to segmet the iris from the uwated parts(eyelids, eyelashes)will be used. 2. Eige Irises will be obtaied usig PCA. 3. Eige irises obtaied will be used to trai the system ad to trasform the traiig irises i to a ew space. 4. Irises i the ew trasformed space will be stored i a database, hece will be used i the recogitio process. II. PRINCIPAL COMPONEN ANALYSIS A. Review Stage he (PCA) ool: Pricipal Compoet Aalysis has bee widely used for aalyzig computer images. It is employed i biometric idustry ad systems as a classificatio tool. PCA does eable someoe to measure a differece betwee two images while allowig expressio chages. I the vector space. PCA performs these by computig the eigevectors ad eigevaluses of a covariace matrix of a image data. Keepig merely a few eigevectors correspodig to the largest eigespace (for details o PCA, refer to [7]). Priciple Compoet Aalysis, also ow as the Eige XY aalysis, ad is a stadard statistical techique for fidig directios of maximum variatios i data. hese directios, called the priciple compoets, where they ca be used to recostruct all of the iformatio withi a data set, ad ca be tested to which level a test image couples with a image of a traiig set. Priciple compoets with slighter associated magitudes ca frequetly be omitted, as they cotribute fewer to the etire recostructio of each data elemet. his allows sufficiet represetatio of the origial data set with a reduced set of pricipal compoets. he pricipal compoets are foud by computig the eigevalues ad eige vectors of the (Covariace Matrix) associated with the data. Eigevectors with largest associated eigevalues are the pricipal compoets that describe the most variatio i the data set. Priciple Compoet coefficiets of data elemets are foud by projectig each datum oto the eigespace of the Covariace Matrix. B. Preparatios of (PCA) I order to employ PCA for iris processig, firstly it is required to covert a RGB (Red-Gree-Blue) scee ito a gray scale (blac ad white) format. It is also possible to use a RGB scee, other tha, istead of usig a (3-D) matrices, (2-D) matrices are eve easier to evaluate. Give a gray scale image, that ca be represeted as a matrix of itesity values. We ca covert it ito a vector by placig the colum of a image o top of each aother. Eye image is a two-dimesioal array of size x.y x, y are, where width ad height of the a image. Each image ca thus be represeted as a vector of dimesio x y: RGB (Red-Gree-Blue) Image vector form of the image Fig. (2) : Obtaiig Vector Form of a Image C. raiig Phase of (PCA) Lettig be a image from a collectio of () images i database. : P,P P 2,, i Fig. (2), are put as P Average image is defied as : P 2 P 3 P () 43

3 Iteratioal Joural of Soft Computig ad Egieerig (IJSCE) ISSN: , Volume-3, Issue-2, May 203 (2) i M i After computig the average colum vector matrices, it is required to build a differeces matrix. Each image differ from the average image by the vector : i for i,2,3,, 2 3 he covariace matrix of the dataset is thus defied by Equ. (4) as: C M C i M (3) (4) After computig the covariace matrix C, we hece build the eigevectors. If we cosider eige vectors u of. Eigevectors u are actually images ad are called eigefaces, amog those eigefaces are the oes with the higher eige values, ad are the most useful i the recogitio process. herefore, those eigefaces M M are used for costructig the eye space for image projectios, which are used i iris idetifyig classifyig or recogizig. D. estig Phase of (PCA) After a traiig phase, ad gettig the eigefaces, we hece move to testig phase to prove our (PCA) theory. I order to chec weather a picture is oe of the samples of our database or ot we should trasform that picture ito eigefaces as follows: u X (5) th is the coordiate of the i ew eye space. Equ. (5) i fact describes the poit by poit image multiplicatios ad summatios resultig i scalar value with dimesios previously defied as weights that defie each face. hose weights form a vector: (6) 2 M Gives the projectio vector. Vector describes cotributio of each eigeface i represetig the iput test images. his vector is used i fidig the class this iput image belogs to, if there are more tha oe images describig a perso, otherwise it is used to fid to which iput image is it much similar, this will be doe by usig eucludiea distace betwee the test image ad projectio image classes. If we compute the distace by usig the ω for the traiig ad test phase, it will give us the distace of the test picture accordig to the traiig phase. 2 (7) th I Equ. (7), describes the eye class, which is average of the eigeface represetatio of the eye images of each idividual. A eye will be classified to some class if the miimum is below some threshold, otherwise it will be classified as a uow picture. E. Fuzzy C-Meas Clusterig: (FCM) Fuzzy C-meas (FCM) is a data clusterig techique, through which each data poit belogs to a cluster with some degree, that is specified by a membership grade. his techique was origially itroduced by Bezde i 98 as a improvemet o earlier clusterig methods. It provides a approach that shows how to group data poits that populate some multidimesioal space ito a specific umber of differet clusters. he MatLab Fuzzy Logic oolbox, commad lie fuctio (FCM) starts with a prelimiary guess for the cluster ceters, which are iteded to mar the mea locatio of each cluster. he prelimiary guess for these cluster ceters is most liely icorrect. Furthermore, (FCM) assigs every data poit a membership grade for every cluster. By iteratively updatig the cluster ceters ad the membership grades for each data poit, (FCM) iteratively moves the cluster ceters to the right locatio cotaied by a data set. his iteratio is based o miimizig a objective fuctio that represets the distace from ay give data poit to a cluster ceter weighted by that data poit s membership grade. (FCM) is a commad lie fuctio whose output is a list of cluster ceters ad several membership grades for each data poit. his ca be used to get the iformatio retured by Matlab routie (FCM) to facilitate buildig a fuzzy iferece system by creatig membership fuctios to represet the fuzzy qualities of each cluster. For istat, while usig some quasi-radom two-dimesioal data, as show i Fig. (3), to illustrate how (FCM) clusterig wors. y Scattred data Eye image desity Data v v2 Clusters ceter Image desity has bee projected clusters Curves of equidistace Fig. (3) : A clusterig of images. A example of a two-dimesioal itese image. Now the commad-lie fuctio, FCM, is ivoed ad ased to fid two clusters i this data set. [ceter,u,objfc] = fcm(fcmdata,2); Iteratio cout =, obj. fc = Iteratio cout = 2, obj. fc = this is cotiued to be computed util the objective fuctio is o loger decreasig much at all. he variable ceter cotais the coordiates of the two cluster ceters, (U) cotais the x 432

4 Pricipal Compoets Aalysis Based Iris Recogitio ad Idetificatio System membership grades for each of the data poits, ad (objfc) cotais a history of the objective fuctio across the iteratios. Fig. (4) displays the two separate clusters classified by the (fcm) routie. he figure is geerated usig Cluster ceters ad are idicated i the figure below by the large characters. Eye image desity First Step: Iris localizatio Secod Step: Polar represetatio y Scattred data Data v v 2 Clusters ceter Image desity has bee projected clusters Curves of equidistace Fig. (4) Data separated ito two differet clusters after clusterig with their cetroid x Fig. (6) : Polar represetatio of the segmeted iris alog with oise masig. B. Chiese Academy of Scieces Dataset, (CASIA) All the iris experimets completed i this paper were performed o the (CASIA) dataset provided by the (Chiese Academy of Scieces), [6]. he (CASIA) dataset cotais some o-ideal iris images of 08 irises with 7 images per oe iris. Images i this dataset are strogly occluded, blurred, ad defocused. Sample images from CASIA datasets are exposed i Fig. (7). Fuzzy C-meas is also used i image segmetati.o, [], by clusterig a set of data based o it s color itesities as show i Fig. (5). Fig. (5) : (left), Real image. (right), segmeted image, Romdhai S. []. III. IMPLEMENAION A. Iris Localizatio, Ecodig, ad Matchig he followig geeral phases are ivolved i most of iris recogitio systems curretly i use : i. Localizatio: Durig this step the pupil, sclera ad eyelids are segmeted. ii. Ecodig: his step ivolves extractio of the most discrimiatig iformatio preset i a iris patter i order to provide accurate recogitio of idividuals. iii. Matchig: he ecoded irises are the stored i the database ad are matched with the test ecoded iris for the purpose of recogitio. o localize a iris, this was achieved by usig the Libor Mase s algorithm of [], as illustrated i Fig. (6). PCA was the used for traiig the system ad extractig the most importat features from irises. Hece, to trasform a iris image ito a ew space. he trasformed irises are stored i a database, where are compared with other irises for recogitio. Fig. (7) : Sample images from CASIA dataset, Chiese Academy of Scieces database, [6]. Left: Smaple_. Ceter: Sample_2. Right: Sample_3. C. Iris Segmetatio Fuzzy C-meas method was attempted to segmet the iris from the eye image. As metioed earlier, Fuzzy -meas partitios the data ito clusters based o their similarities. While eepig this i mid, the eye image show i Fig. (8) was coverted to (RGB) ad was clustered based o color to four differet parts. It was assumed that the si will tae the first cluster, sclera the secod, iris the third ad pupil the fourth. Result of each clustered iris ca be show i Fig. (9). Fig. (8) he origial eye. A sample of a eye iris to be clustered. Further processig steps are give i Fig. (9). 433

5 Iteratioal Joural of Soft Computig ad Egieerig (IJSCE) ISSN: , Volume-3, Issue-2, May 203 (a) (c) (d) Fig. (9) : Processed iris ito differet clusters. (a): Clustered si. (b): Clustered sclera. (c): clustered iris. (d): clustered pupil. D. Eige Irises Geeratio he first objective is to acquire the eige irises. hese will be the be used to trai a ANN system. he eigeiris geeratio implemetatio is ot the oe suggested i sectio (2). I fact, the used eigeirises are the eigevectors of the matrix XX. Adoptig the otatios of sectio (2), X is a matrix cotaiig the iput irises - each colum represetig a colum iris vector. he dimesio of X is I (umber of pixels of each iris) by I (umber of faces i the traiig set). As has bee oted, the umber of irises i a traiig set is usually smaller tha the umber of pixels cotaied i each iris image. For example, each iris cotais (4800) pixels. Hece, the dimesio of matrix XX is I I, where I his is a waste of both processig time ad memory space to compute the eigevectors by this basic method of SECION (2). I this sese, a more efficiet techique will be preseted here. Istead of computig the outer product XX, the ier product, X X, is this calculated. Eigevectors of the outer product are deduced from those of the ier product. Assumig the followigs : (b) V be the outer product, XX, i. the matrix ii. the matrix Q be the matrix of the eigevectors of V, iii. the matrix P be the matrix of the eigevectors of the outer product, XX, iv. ad the diagoal matrix be the matrix of the eigevalues of the ier product ad of the outer product, Dorairaj et. al. []. his results i P computed as ; XQ P (7) Each iris image is coverted i to a sigle colum vector as was show previously i Fig. (2). hese colum images are used to obtai the eige irises as illustrated here i Fig. (0). Fig. (0) : he first six eige irises, Dorairaj et. al. []. E. Iris Space Oce the eigeirises have bee computed, each iris i the image space ca be viewed i the trasformed space. he trasformatio from image space to trasformed space is fairly simple. Lettig the followigs : E be the matrix of the first (eigeirises), where the first i. colum is the first eigeiris ad so o. e be a face i the image space, ad. ii. i iii. e be the same iris i the trasformed space. e e E i his is a may-to-oe trasformatio, sice the dimesioality of the image space is far larger tha the dimesioality of trasformed space. F. he Recogitio Process Oce the eigeirises have bee computed, the trasformed space has to be populated with several ow irises. Usually these irises are iputted from the used traiig set. Each ow iris is trasformed ito the ew space, where its compoets are stored a database. At this stage, the idetificatio process ca be iitiated. A uow iris is preseted to the system. he system project it oto the trasformed space ad computes its distace from all the stored irises. he iris is idetified as beig the same idividual as the iris which is earest to it i trasformed space. here are several methods of computig the distace betwee multidimesioal vectors. Here, a mea of the distace betwee the two trasformed irises is computed ad the shortest mea gives the best match. he overall idetificatio process ca be summarized i Fig. (). G. he Etire Developed System Overview he software to perform iris recogitio was developed usig the MALAB developmet eviromet. A computer system with a Itel Petium IV processor ruig at (.7 GHz) was used. he program is composed of two MALAB fuctios - (get_p_irisdb.m) ad (test.m) the former fetches the eige vectors ad the database of the trasformed irises, while the latter fuctio is used for testig as show Fig. (2). Fig. () A flowchart summary of employed iris recogitio process. 434

6 FALIURE RAE FALURE RAE Pricipal Compoets Aalysis Based Iris Recogitio ad Idetificatio System MatLab Based Recogitio Eviromet Itel Petium IV processor ruig at.7 GHz Eigevectors ad Iris Databases Iris Extractio Phase ad raiig Phase For istat, i Fig. (3) more (30%) of the iris is ot accessible by the system due to the eyelid ad eyelashes herefore achievig such a success rate reveals that iris recogitio is i fact a very reliable biometric verificatio system. est results also show that Pricipal Compoet aalysis is robust feature extractio techique that ca be used i iris recogitio systems. Observatio was doe by reducig the umber of traiig images as show i Fig. (4). estig stage estig image of a iris Results: idetified iris 8 (i) RAINING IMAGES PER INDIVIDUL Fig. (4) Failure rate versus traiig images. (ii) NUMBER OF EIGEN IRISES (iii) Fig. (2) (i) Employed System Overview. (ii) Locally built iris database. (iii) Segmet of used Matlab codig eviromet. IV. RESULS VALIDAION Matlab computig eviromet was used to test the proposed algorithm. I order to verify the proposed idetificatio methodology, ad achieve some testig o the CASIA dataset, four differet irises have bee used durig the traiig phase. I additio, three differet images have bee used durig the testig phase (as per idividual). A success rate of (92.65%) was achieved. I additio, we have to bear i mid that the CASIA dataset cotais highly o-ideal, occluded images as show i Fig. (3). Fig. (3) estig phase: Eyes are imported from the used freely available CASIA dataset, Chiese Academy of Scieces, [6]. Fig. (5) Failure rate versus the used eige irises. A reductio i the umber of traiig images resulted i a icrease i the false idetificatio by the adopted system. his is mostly due to the fact that iris images whe tae i differet positios are slightly out of phase. herefore multiple iris images of the same idividual i the database icreases the idetificatio. Daugma [3], however came up with a solutio to this problem by maig a algorithm that taes the possibility of phase shift i to cosideratio, by which a sigle clear iris image per perso i the database is adequate to idetify the idividual idepedet of the positio he/she is i. Aother observatio was made by decreasig the umber of eige irises used as show i Fig. (5). he umber of eige vectors required geerally depeds o the data beig used [0]. We have reached the best results whe the umber of eige irises where M, where M is the total umber of database irises used for traiig. he umber of eige irises required for good result shows us how rich ad less compressible is the patter of the irises [0]. For more irises ad further aalysis, this ca be foud i [2]. V. CONCLUSIONS his research article has preseted a iris recogitio 435

7 Iteratioal Joural of Soft Computig ad Egieerig (IJSCE) ISSN: , Volume-3, Issue-2, May 203 system. he system was tested usig grey scale images. he eye images were highly o-ideal ad a good portio of the irises were hidde by eyelids ad eyelashes. Firstly, a automatic segmetatio algorithm is used to (localize) the iris ad (segmet) it from the uwated parts of the eye (eyelash, eyelid). Subsequetly (PCA) was applied to do a ANN traiig irises to obtai the Eige irises, these eige irises were used to trasform a origial iris image ito a ew MM trasformed space which had the dimesios of, where M is the umber of iris images used to trai the system. hese trasformed irises were (stored) i a iris database ad were used to (compare) the test irises which had bee trasformed i the same space to perform the recogitio process. he research results have achieved a success rate of (92.65%) with a highly o ideal iris images. o this extet, this verifies the high reliability of the iris beig used i biometric recogitio ad highlights the robustess of PCA i image recogitio. REFERENCES [] L. Flom, ad A. Safir, "Iris Recogitio System". U.S. Patet, (464394), (987). [2] S. Saderso, ad J. Erbetta, "Autheticatio For Secure Eviromets Based o Iris Scaig echology," IEE Colloquium o Visual Biometrics, UK, (2000). [3] J. Daugma, " How Iris Recogitio Wors," Proceedigs of 2002 Iteratioal Coferece o Image Processig, vol., (2002). [4] E. Wolff, "Aatomy of the Eye ad Orbit.", 7 th Editio. H. K. Lewis ad Co. LD, (976). [5] R. Wildes. "Iris Recogitio: A Emergig Biometric echology, " Proceedigs of the IEEE rasactios, vol. 85, o. 9, (997). [6] Chiese Academy of Scieces Istitute of Automatio. Database of 756 Grey Scale Eye Images. Versio.0, (2003). [7] Iridia echologies [8] A. ur, ad A. Petlad, Face Recogitio Usig Eigefaces, Proceedigs of the IEEE Coferece o Computer Visio ad Patter Recogitio, pp , Hawaii, Jue (992). [9] L. Mase, "Recogitio of Huma Iris Patters for Biometric Idetificatio", Bachelor of Eg. hesis, the Uiversity of Wester Australia, (2003). [0] S. Romdhai, "Face Recogitio Usig Pricipal Compoet Aalysis," Published M.Sc. hesis, Uiversity of Glasgow, (996). [] V. Dorairaj, A. Natalia, ad G. Fahmy, "Performace Evaluatio of No-Ideal Iris Based Recogitio System Implemetig Global ICA Ecodig, IEEE Iteratioal Coferece o Image Processig, vol. 3, Issue, (-4), Page(s): III , (2005). [2] M. A Hameed, "Iris Recogitio Usig Pricipal Compoet Aalysis", echical Project Report, College of Egieerig, Uiversity of Bahrai, (2007). E. Mattar is a associate professor of itelliget cotrol ad robotics at Uiversity of Bahrai, Bahrai. He has received BSc. i Electrical Egieerig (from Uiversity of Bahrai i 986), studied MSc. i Electroics i 989 (from Uiversity of Southampto, UK), ad i 994 he received Uiversity of Readig Ph.D. i Cyberetics ad Robotics Cotrol. Dr. Mattar has iterests i computatioal itelligece, robotics cotrol, modelig ad cotrol. 436