Biometric System Design for Iris Recognition Using Intelligent Algorithms

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1 I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6 Published Olie March 8 i MECS ( DOI:.585/ijmecs.8.3. Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms Muthaa H. Hamd Al-Mustasirya Uiversity/ Computer Egieerig Departmet, Baghdad,, Iraq dr.muthaa.hamd@gmail.com Samah K. Ahmed Al-Mustasirya Uiversity/ Computer Egieerig Departmet, Baghdad,, Iraq samah357@gmail.com Received: 8 September 7; Accepted: 5 Jauary 8; Published: 8 March 8 Abstract A iris recogitio system for idetifyig huma idetity usig two feature extractio methods is proposed ad implemeted. he first approach is the Fourier descriptors, which is based o trasformig the uiqueess iris texture to the frequecy domai. he ew frequecy domai features could be represeted i irissigature graph. he low spectrums defie the geeral descriptio of iris patter while the fie detail of iris is represeted as high spectrum coefficiets. he priciple compoet aalysis is used here to reduce the feature dimesioality as a secod feature extractio ad comparative method. he biometric system performace is evaluated by comparig the recogitio results for fifty persos usig the two methods. hree classifiers have bee cosidered to evaluate the system performace for each approach separately. he classificatio results for Fourier descriptors o three classifiers satisfied 86% 94%, ad 96%, versus 8%, 9%, ad 94% for priciple compoet aalysis whe Cosie, Euclidea, ad Mahatta classifiers were applied respectively. hese results approve that Fourier descriptors method as feature extractor has better accuracy rate tha priciple compoet aalysis. Idex erms Iris recogitio, Fourier descriptors, Priciple compoet aalysis, feature extractio. cotais erichmet features that would be extracted later to geerate the patters. he biometric system based iris recogitio is reliable ad moder techique for prevetig frauds ad fakes operatios []. his paper proposes ew methods for buildig biometric system based iris recogitio; it uses two comparative procedures to extract the iris feature ad geerate the iput machie vectors. hese methods are the Fourier descriptors (FDs) ad Priciple Compoet Aalysis (PCA). FD describes the object cotour by a set of umbers which represets the frequecy cotet for full form, so ay twodimesioal object could be ecoded by trasformig its boudary ito frequecy domai complex umbers. he challege is o previous research uses the shape descriptor procedure as a feature extractor i the frequecy domai. he PCA is a static techique that widely used i image compressio ad face recogitio applicatios for decreasig the data dimesioality. he paper could be orgaized as follows: sectio I is the itroductio, sectio II ad III explai the FD ad PCA procedures ad the proposed iris recogitio system respectively. Sectio IV tabulates the recogitio results of three distace measure methods; it shows the accuracy rate for each method that was applied o 5 iris images. Evetually, sectio V discusses ad cocludes the highest accuracy result ad its related three distace measures. I. INRODUCION he traditioal security techiques such as, passwords, codes, ad ID cards are ot cofidet i may applicatios so the demads for biometric systems has bee icreased to support ad ehace the traditioal security systems. Humas have may traits that ca be used i a biometric system to idetify their idetity ad hece to give them a authorizatio to access for example their bak accouts or eve to pass the smart gate i airports. raits could be face, figerprits, voice, or iris. Biometric system based iris has may advatages over the others recogitio system like face, voice, or figerprit. Iris is stable through all huma life, it does ot affect by geetic gee or feelig of perso. Also, it II. REVIEW OF RELAED WORK I 936 the ophthalmologist Burch suggested the idea of utilizig the iris i huma idetificatio. Ara ad Flom i 987 adopted Burch s idea of idetifyig people based o their idividual iris feature. I 4, J. Daugma applied Gabor filters to create the iris phase code, he registered excellet accuracy rate o differet umber of iris datasets. He used Hammig distace betwee two bits for phase matchig []. I 4, So et al., used liear discrimiat aalysis (LDA), Direct Liear Discrimiate Aalysis (DLDA), a Discrete Wavelet rasform (DW), ad PCA ad to extract iris features []. I 7, R. Al-Zubi ad D. Abu-Al-Nadi applied a circular Hough trasform ad Sobel edge Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

2 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms detector i segmetatio process to fid the pupil's locatio. Also, Log-Gabor filter is applied to geerate the feature vectors. his method achieved 99% accuracy rate [3]. I 8, R. Abiyev ad K. Altukaya suggested a fast algorithm for localizatio of the ier ad outer boudaries of iris regio usig Cay edge detector ad circular Hough trasform, also, a eural etwork (NN) was suggested for classificatio the iris patters [4]. I, the iris regio was ecoded usig Gabor filters ad Hammig distace by S. Nithyaadam [5]. I, Ghodrati et al. proposed a ovel approaches for extractig the iris features sig Gabor filters. he Geetic algorithm was proposed to compare two differet templates [6]. Gabor approach was more distictive ad compact o feature selectio approach. I 3, G. Kaur [7] suggested two differet methods usig the Support Vector Machie (SVM). SVM results were FRR=9.8%, FAR = %, ad overall accuracy rate = 99.9%. Choudhary et al. i 3 [8], proposed a statistical texture feature depeded iris matchig method usig K-NN classifier. Statistical texture measures iclude stadard deviatio, mea, skewess, etropy etc., K-NN classifier matches the curret iput iris with the traied iris images by computig the Euclidea distace betwee two irises. he system is tested o 5 iris images, which gives good classificatio accuracy rate with reduced FRR/FAR. Jayalakshmi ad Sudaresa i 4 [9] proposed K- meas algorithm ad Fuzzy C-meas algorithm for iris image segmetatio. he two algorithms were executed separately ad the performaces of them were 98.% of accuracy rate with low error rate. I 5, Homayo [] suggested a ew method based eural etwork for iris recogitio. he proposed method is implemeted usig LAMSAR classifier that utilized CASIA-v4. database. he accuracy rate was 99.57%. I 6, A. Kumar ad A. Sigh suggested a ovel method for extractig the feature ad the recogitio was implemeted o D discrete cosie trasform (DC). hey applied the DC to extract the most discrimiated features i iris []. he patters have bee tested o two iris datasets; IIID ad CASIA v.4. for matchig the iris phase usig Hammig distace. he accuracy rate were 98.4% ad 99.4% for IID ad CASIA V4 database respectively. III. IRIS RECOGNIION SYSEM he proposed system cosists of five stages: iris-image acquisitio, iris/pupil detectio ad segmetatio, iris ormalizatio, feature geeratio, ad patter recogitio. Figure ad 3 shows the flowchart for iris recogitio system usig two feature extractor methods, the FD ad PCA. 3.. Image Acquisitio he biometric system is costructed usig Chiese Academy of Scieces Istitute of Automatio (CASIA) versio database. he image is 3 8 pixels take from a distace of 4-7 cetimeters. 3.. Iris detectio, localizatio, ad Segmetatio Iris/pupil detectio state passes through may operatios like smoothig, biarizatio, ad localizatio. he segmetatio state icludes two operatios: pupil localizig ad iris localizig, the two steps are: A. Pupil localizatio he pupil regio ca be detected ad localized by the followig steps: Step: image smoothig usig the o-liear media filter Step: biarizatio as i (). F(x,y) f(x,y) =τ f(x,y) τ Step3: oise removal usig mathematical morphology: Opeig ad closig operatios Step4: lik the edges by applyig the coected compoet labellig algorithm to Step5: Fid the pupil ceters, P x ad P y as i () ad (3), the fid the maximum row ad colum vector as i (4) ad (5). See Fig.. Where: h(x) x all rows y v(y) all cloums Px Py () () (3) max (h) (4) max (v) (5) x, y: is the o. of rows ad colums respectively Step6: Fid the radius of the pupil (r p ), as i (6) rp B. Iris Localizatio ( max (h) mi (h))/ (6) Cay masks are very useful i locatig the iris boudary while the circular Hough trasform is applied to complete the circle shape of iris as show i Fig Iris Normalizatio Normalizatio process miimizes the distortio resultat from pupil motio; it is a vital step for good verificatio result. First, the iris regio is coverted from circular shape to a rectagular oe, as Fig.5. he rubber sheet model which is fouded by Daugma is applied to covert the Cartesia coordiates to polar form model. Polar form provides pixels alog r ad 4 pixels alog θ so a uwrapped strip of 4 size will stay ivariat for image skewig or extesio. Equatios (7) Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

3 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms ad (8) represet a map fuctios from Cartesia coordiates to polar form (r, θ) are []: X(r,θ ) ( r)*x p (θ) r*x i (θ) (7) Where, Where: Y(r,θ ) ( r)*y p (θ) r*y i (θ) (8) X p (θ ) X po (θ) r p * cos(θ) (9) Yp (θ ) Y po (θ) r p * si (θ) () Xi (θ ) X io (θ) r i * cos(θ) () Yi (θ ) Y io (θ) r i * si(θ ) () (X p, Y p ): is the ceter of the pupil ad iris, (r p, r i ): is the radius of the pupil ad iris. Figure 6 shows the iris ormalizatio ad smoothig result. Fig.. Iris recogitio system procedure usig PCA (a) Origial Image (b) Apply Media Filter (c) Biary Image (d) Morphological Operatio Fig.. Iris recogitio system procedure usig FD Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

4 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms (a) Iris ormalizatio (e) Coected Compoet Labelig (f) Max Row (b) De-oisy image Fig.6. Iris ormalizatio P y (g) Max Colum (h) Pupil Localizatio Fig.3. Pupil localizatio steps (a) Origial Image (b) Biary Image 3.4. Feature Extractio usig wo echiques wo differet techiques i feature extractio step are applied to create the templates ad iput vector machie for matchig process. A. Fourier Descriptors here are may techiques for templates creatio such: discrete wavelet trasform; zero-crossig wavelet; spatial filters; local biary patter; local variace; D local texture patter ad Gabor wavelet. I this work, the FD is used to geerate the feature vectors by calculatig coefficiets of the trasformed iris image i frequecy domai. he high-frequecy coefficiets represet the iformatio cocerig precise details of the object while the low-frequecy coefficiets represet the iformatio cocerig the geeral traits of the object. he FD has bee successfully used for may applicatios of the shape represetatio. he FDs have good characteristics, such as it has robustess to oise, simple derivatio ad simple ormalizatio. he umber of geerated coefficiets from the shape trasformatio is commoly large, so sufficiet coefficiets ca be chose to represet the object features [, 3]. he FD procedure is: cout boudary poits, select samplig umber (N), calculate cetroid distace as i (3), r(t) [x(t) x c ] [y(t) y c ] (3) Where; (c) Cay Edge Filter Fig.4. Iris localizatio steps (d) Iris Localizatio N N x c x( t), y c y( t) N t N t he cetroid distace idicates to the positio of the shape from the boudary coordiates [3]. Calculate Fourier trasforms values, as i (4), ad the ormalize the FD coefficiets as i (5). FD = N r(t)* exp ( N t jππ ) N (4) Fig.5. Daugma s rubber sheet model where =,, N-. Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

5 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms 3 FD FD FD N- f=,,.. (5) FD FD FD he umber of the FD coefficiets is large (5 coefficiets), thus must be decreased to (5 coefficiets) for makig the proposed system simple ad fast, as i Fig.7. hat represets the diagoal covariace matrix Y. Eigevectors must be liearly idepedet. λ Y λ λ (8) Step3: compute the trasformatio matrix, which represets the eigevectors as their colums i (9).,,,,,, i,,, (9) Step4: calculate the trasform features, CY = C x W ' or CY = W C x ' () Fig.7. Iris Sigature B. Priciple Compoet Aalysis his approach is kow as the Hotellig trasform. he major idea of applyig PCA is to extract the feature templates. PCA utilizes the factorizatio to covert data ito its statistical properties. his techique is useful for compressio, classificatio ad reductio of dimesios. Pricipal compoets are a mathematical process, which trasforms umber of correlated variables ito smaller umber of ucorrelated variables [4]. PCA procedure is: Step: Calculate the covariace matrix X (which X represet feature vectors), as i (6) ad (7). he features must be chose with large values of (λi) []. Step5: ormalize the features trasformatio (C Y ), see Fig. 8. C C C N C,,., () C C C N: represets the umber of trasform features X σ x,, σ x,, σ x,, σ x,, σ x,, σ x,, σ x,, σ x,, σ x,, (6) c c x,i μ x,i x,j μ x,j σ x,i,j (7) Where, c x,i,j : represet the feature vectors μ x,i,j : represet the mea vector Step: compute the eigevalues ad eigevectors by usig characteristic equatio: det( i I X ) Patter Matchig Fig.8. PCA features represetatio Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

6 4 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms I matchig stage, 3 iris-images were selected for 5 persos; each perso has 6 samples i traiig phase. While i testig phase, 5 iris-images for 5 persos were take. hese images are chose from CASIA-v database. CASIA-v close-up iris camera is used for capturig iris images. he resolutio of these images is 3*8 with cross-sessio iris images ad extremely clear iris texture details. Mahatta distace, Cosice distace, ad Euclidea distace are applied ad programmed for executig the classificatio. he measures distace methods [5] are applied to fid the distace betwee the curret template vector ad each traiig templates vectors stored i database. he miimum distace should be checked; if it is less tha threshold value that meas it is from the same class otherwise it belogs to various classes, the followig measures distace ways are: Cosice distace (X Y) d(x,y) i () Y X i i PCA for three distace approaches (Mahatta, Euclidea ad Cosie). his table shows the total ruig time of iris recogitio system for all three distace measuremet ways. No. of accepted imposter FAR * % (5) otal o. of imposter assessed No. of rejectio geuie FRR * % (6) otal o. of geuie assessed Where, Acc ( Nc )*% (7) N N C : represets to the umber of correct iris samples. N : is the total umber of iris samples. Euclidea distace d(x,y) (X i Y i ) i (3) Mahatta distace d(x,y) i X i Y i (4) IV. EXPERIMENAL RESULS he proposed system is tested for 5 persos; each oe has 6 images for traiig ad oe image for testig. he iris regio is isolated by usig some operatios such as: morphology operatios ad coected compoet labelig algorithm which applied to fid the pupil parameters (i.e. radius ad ceter). Cay edge detector ad circular Hough trasform are applied for fidig the iris parameters. he, iris regio is coverted from the Cartesia to polar coordiate by Daugma rubber sheet model. herefore, the rectagular iris image is used for feature extractio as FD ad PCA. hree measured distace methods are used for classificatio. Figure 9 ad show the verificatio performace plots of FD ad PCA methods respectively. hese figures show that the Mahatta advaces Euclidea ad Cosie distace for both FD ad PCA. hree importat stadards are utilized for the performace evaluatio measuremet, they are: False Reject Rate (FRR), False Accept Rate (FAR), ad Accuracy rate (Acc) as described i (5, 6, ad 7). able displays the testig performaces of FRR, FAR, ad Acc rate for iris verificatio system usig FD ad Fig.9. Performace compariso betwee Mahatta, Euclidea ad Cosie distace for FD Fig.. Performace compariso betwee Mahatta, Euclidea ad Cosie distace for PCA Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

7 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms 5 able. Performace compariso betwee Fourier descriptors ad priciple compoet aalysis for 5 persos Method FD PCA Distace measures FAR% FRR% Acc% ime of traiig (sec) Mahatta Euclidea Cosie Mahatta Euclidea Cosie V. CONCLUSION A biometric iris recogitio system is proposed ad implemeted usig three types of classifiers (Cosie, Euclidia, ad Mahatta) ad two feature extractor methods to demostrate the most effective oe i recogizig the iris ad the defiig the huma idetity. FD was the first feature extractio procedure that chooses the sufficiet coefficiets from iris sigature graph. he secod approach is the PCA where a collectio of orthogoal basis vectors are created after decreasig the dataset dimesioality to select the most sigificat feature vectors. he iris localizatio ad segmetatio provided % accuracy rate. his work has approved that the performace of FD i iris recogitio is most effective tha PCA. It satisfied 96%, 94% ad 86% accuracy rate for Mahatta, Euclidea ad Cosie classifiers respectively, while PCA satisfied 94%, 9% ad 8% for same classifiers respectively. Mahatta classificatio results were the best oe amog Euclidea ad Cosie classifiers for both FD ad PCA approaches. REFERENCES [] J. Daugma, "How iris recogitio works?", IEEE rasactios o Circuits ad Systems for Video echology, Vol. 4, No., pp. 3, Jauary 4. [] B. So, H. Wo, G. Kee, Y. Lee, Discrimiat Iris Feature ad Support Vector Machies for Iris Recogitio, i Proceedigs of Iteratioal Coferece o Image Processig, vol., pp , 4. [3] R.. Al-Zubi ad D.I. Abu-Al-Nadi, "Automated Persoal Idetificatio System Based o Huma Iris Aalysis", Patter Aalysis ad Applicatios, Vol., pp , 7. [4] R. Abiyev ad K. Altukaya, "Persoal Iris Recogitio Usig Neural Network", Iteratioal Joural of Security ad its Applicatios, Vol., No., pp. 4-5, April 8. [5] S. Nithyaadam, K. Gayathri ad P. Priyadarshii, "A New IRIS Normalizatio Process For Recogitio System With Cryptographic echiques", IJCSI Iteratioal Joural of Computer Sciece, Vol. 8, Issue 4, No., pp , July. [6] H. Ghodrati, M. Dehghai, ad H. Dayali, "wo Approaches Based o Geetic Algorithm to Geerate Short Iris Codes", I.J. Itelliget Systems ad Applicatios (IJISA), pp. 6-79, July. [7] G. Kaur, D. Kaur ad D. Sigh, "Study of wo Differet Methods for Iris Recogitio Support Vector Machie ad Phase Based Method", Iteratioal Joural of Computatioal Egieerig Research, Vol. 3, Issue 4, pp , April 3. [8] D. Choudhary, A. Sigh, ad S. iwari, "A Statistical Approach for Iris Recogitio Usig K-NN Classifier", I.J. Image, Graphics ad Sigal Processig (IJIGSP), pp. 46-5, April 3. [9] S. Jayalakshmi ad M. Sudaresa, "A Study of Iris Segmetatio Methods usig Fuzzy C Meas ad K- Meas Clusterig Algorithm", Iteratioal Joural of Computer Applicatios ( ) Vol.85, No, Jauary 4. [] S. Homayo, "Iris Recogitio For Persoal Idetificatio Usig Lamstar Neural Network", Iteratioal Joural of Computer Sciece & Iformatio echology (IJCSI) Vol. 7, No, February 5. [] A. Kumar, A. Potis ad A. Sigh, "Iris recogitio ad feature extractio i iris recogitio system by employig D DC", IRJE Iteratioal Research i Computer Sciece ad Software Egieerig, ad echology, Vol.3, Issue, p. 53-5, December 6. [] M. Nixo ad A. Aguado, "Feature Extractio & Image Processig for Computer Visio", third editio, AP Press Elsevier,. [3] A. Amaatiadis, V. Kaburlasos, A. Gasteratos ad S. Papadakis, "Evaluatio of shape descriptors for shapebased image retrieval", he Istitutio of Egieerig ad echology, Vol. 5, Issue 5, pp ,. [4] Saporta G, Niag N. Pricipal compoet aalysis: applicatio to statistical process cotrol. I: Govaert G, ed. Data Aalysis. Lodo: Joh Wiley & Sos; 9, 3. [5] R. Porter, "exture Classificatio ad Segmetatio", Ph.D thesis, Uiversity of Bristol, November 997. Authors Profiles Muthaa H. Hamd post-doctoral degree, Ui. of Wollogog, Australia 7-9, Ph. D, Ui. of Baghdad, Iraq 4, M. Sc. Ui. of Baghdad, Iraq 998. Academic staff member i Computer Egieerig Al-Mustasirya Uiversity. Iterested area: digital image processig, computer visio, eural etworks, feature extractio, ad itelliget algorithms. algorithms. Samah K. Ahmed was bor o May, 993. Master of Sciece studet, Computer Egieerig AL Mustasirya Ui. 6, B. Sc., Computer Egieerig AL Mustasirya Ui. 5. Iterested area: digital image processig, eural etworks, feature extractio, classificatio ad itelliget Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6

8 6 Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms How to cite this paper: Muthaa H. Hamd, Samah K. Ahmed, " Biometric System Desig for Iris Recogitio Usig Itelliget Algorithms", Iteratioal Joural of Moder Educatio ad Computer Sciece(IJMECS), Vol., No.3, pp. 9-6, 8.DOI:.585/ijmecs.8.3. Copyright 8 MECS I.J. Moder Educatio ad Computer Sciece, 8, 3, 9-6