FEATURES VECTOR FOR PERSONAL IDENTIFICATION BASED ON IRIS TEXTURE

Transcription

1 FEATURES VECTOR FOR PERSONAL IDENTIFICATION BASED ON IRIS TEXTURE R. P. Moreo Departameto de Egeharia Elétrica EESC - USP Av. Trabalhador Sãocarlese, 400 São Carlos / SP Brasil raphael@digmotor.com.br A. Gozaga Departameto de Egeharia Elétrica EESC - USP Av. Trabalhador Sãocarlese, 400 São Carlos / SP Brasil adilso@sel.eesc.sc.usp.br Abstract This work presets a biometric method for idetificatio vector buildig based o huma iris features. The proposed work is based o iris texture features aalysis ad extractio. The work is divided i 3 steps. I the first, the eye image is preprocessed ad Hough Trasform for circles does the iris localizatio ad segmetatio. I the secod step, the iris features iformatio is extracted by a secod order statistical approach, usig the Haralick s texture features as classificatio parameters. Fially i the last step, the iformatio is saved i a feature vector that ca be used for iris recogitio. Keywords: Haralick, patter recogitio, biometrics, iris, texture. 1 Itroductio Biometry is the group of automatic methods used i people recogitio, based i physiological or behavioral features. Examples of behavioral features are sigature, gait, voice, etc. Examples of physiological features are figerprit, face, iris, had geometry, the veis i ocular retia, etc. Oe of the biometric advatages, if it is compared with covetioal methods, is the possibility of idetify, autheticate ad localize people without requirig that they carry cards or memorize passwords [1]. Recetly, the umber of studies ad researches i iris recogitio has icreased sigificatly. This crescet icrease happes because, i idetificatio systems, iris is more efficiet, stable ad accurate tha the others biometric features [2]. The iris is the circular ad retractile membrae, which is localized i the ceter behid the ocular globe. It s situated betwee the corea ad the aterior part of crystallie, ad it has a orifice, the pupil. The fig. 1 shows the iris positio i relatio to a perso eye. Fig. 1. Eye aatomy. 110

2 Formed by a multi-layer structure, the iris has a very complex color ad shape patter. It ca be observed i fig. 2. Fig. 2. Iris structure see i a frotal sector. The huma iris possibility of bee used as a biometric sigature was first suggested by ophthalmologists [3]. They verifie through cliical experiece, that each iris had a very detailed texture. Recogitio biometric systems based o iris study are possible because of some features. The most importat of them is the iris uiqueess, which is a result of the chaotic orgaizatio of its patters, established by the iitial coditios i the embryoic geetic, [4]. The probability of two people havig the same iris patter is estimated i oe i people. As writte i [5], the right ad left eyes of the same perso have differet texture patters. Aother importat feature is the iris stability. A ormal iris is usually lubricated ad preserved by the corea ad aqueous humor, becomig oe of the most protected orgas i a huma body. Besides, the localizatio, size, shape ad orietatio remai stable ad fixed from about oe year of age throughout life [6]. 2 Iris localizatio The iris localizatio i a image is the task of fid a rig situated betwee the pupil ad the sclera. It is equivalet to fidig o-cocetric circles which determiate the iteral ad exteral borders of the rig. The method used i this work fids the ceter coordiate ad the ray of the pupil, which is the iteral border of the iris, through the Hough Trasform (HT) for circles [7]. Compared with all others parts i the image, the pupil is much darker. So, after the applicatio of a threshol followed by a edge detector, the image will be ready to the Hough Trasform techique. The width of the iris rig used is fixe separatig just the iris regio ear the pupil. Due to partial iris occlusio by the eyelid ad eyelashes, the upper part of the iris rig was removed ad it is ot used i the algorithm sequece. The fig. 3 shows a origial image (a) ad the same image after the iris localizatio (b). 111

3 Fig. 3. (a) Origial image. (b) Segmeted iris. After the pupil ad cosequetly the iris localizatio, the system becomes robust to the pupil size ad the positio of the eye i the image. 3 Haralick s features I this work, it is proposed a iris feature extractio methodology based i the Haralick s approach [8]. It uses secod order statistics, by aalyzig the relative positio of the image pixels. Through this metho distict images with equal first order histograms still ca be differetiated. The secod order statistical measures are doe i probabilities distributios or co-occurrece matrixes. These matrixes (GLCM gray level co-occurrece matrix) are bi-dimesioal represetatios showig the spatial occurrece orgaizatio of the gray levels i a image. They represet a bi-dimesioal histogram of the gray levels, where fixed spatial relatio separates couples of pixels, defiig the directio ad distace () from a refereced pixel to its eighbor. To build these matrixes, the couple of pixels variatio is doe i the followig agles: 0, 45, 90 e 135, origiatig four distict co-occurrece matrixes. After computig the co-occurrece matrixes, several secod orders statistical calculus ca be calculate icludig the Haralick s features. These are the features used i this work: Secod Agular Momet (SAM): measures the local homogeeity of gray levels i a image. The SAM equatio is give by: SAM P ( d, ) i 0 i0 Cotrast: it measures the local quatity of gray levels i a image. The Cotrast equatio is give by: Cotrast ( i j) P( ) (2) Etropy: also called as dispersio degree of the gray levels, it measures together with the SAM, the homogeeity i a image. The Etropy equatio is give by: Etropy 1 1 i0 i 0 P( (1) ) log2 P( ) (3) Iverse Differece Momet (IDM): The IDM equatio is give by: IDM P( ) 2 (4) 1 ( i j) i0 i0 Correlatio: it represets the liearity depedece of gray levels i a image. The Correlatio equatio is give by: 112

4 Where, 1 i j P i j d x y Correlatio i i 1 (,,, ) 0 0 (5) x y 1 1 x i P( ), 1 1 y j P( ) x i P( ) x, y j P( ) y A x ad y represet the mea i X ad Y directio, ad y ad y represet the variace. 4 Feature vector I this work, the segmeted iris is divided ito six sectors havig the same size, as showed i fig. 4. The umber of sectors was defied to icrease the classifyig method efficiecy through the texture features. Fig. 4. Segmeted iris divided i six sectors. For each sector, the five Haralick s features are calculated resultig i a feature vector with 30 values. This vector will be saved i the database or used i a idetificatio or autheticatio process. 5 Image database The image database used to test the algorithm, CASIA versio 1.0 [9], was developed by the Iris Recogitio Research Group - Natioal Laboratory of Patter Recogitio (NLPR) from the Istitute of Automatio, Chiese Academy of Scieces. The dataset has images with 256 gray levels, ad resolutio of 320x280 pixels, captured through a digital optical sesor also developed by the NLPR. There are 756 images of 108 eyes from 80 people. I this dataset, seve images were take from each iris, i two differet momets. I the first oe, three images were take ad i the secod momet, oe moth later, more four images were take. 6 Tests ad results The algorithm fids the proximity of two irises calculatig the ormalized Euclidia distace of the two features vectors, as described i equatio 6. ( A, B) 30 i 1 2 A i B i Ai D (6) 113

5 The fig. 5 shows a example of the distace calculus betwee a iris ad the others 107 restig i the database. Fig. 5. Euclidia distace example. For each compariso betwee two irises' images, the algorithm returs a umber. To show if the vectors are from the same iris, the algorithm compare the value retured with a t (threshold) value, previous established. With this iformatio, it's possible to evaluate the system accuracy varyig the t value ad buildig the ROC curve (receiver operatig characteristic). To build the ROC curve, it was geerated a dataset with the mea feature vector take from each oe of the 108 irises of the database. The mea vectors were obtaied calculatig the meas amog the seve features vectors from each image of the same iris. After that, for each t value it was doe a autheticatio try betwee each database image ad the others 107 irises. As the database has 756 images, autheticatio tries were doe. Durig the autheticatio tries, the umber of false accepted (FA) ad false rejected (FR) were foud. The Table 1 ad the figure 6 show the FA ad FR probability's distributio with the t variatio. Table 1. False accepted probability, P (FA) ad false rejected probability, P (FR). t P (FA) P (FR) 0,00 100,000% 0,000% 0,05 47,153% 3,571% 0,10 43,496% 3,704% 0,15 39,726% 4,101% 0,20 35,634% 4,762% 0,25 31,469% 6,085% 0,30 27,150% 7,011% 0,35 22,927% 7,804% 0,40 18,671% 9,392% 0,45 14,532% 11,243% 0,50 10,673% 13,889% 0,55 7,347% 17,989% 0,60 4,604% 22,354% 0,65 2,479% 27,910% 0,70 1,024% 35,185% 0,75 0,307% 47,222% 0,80 0,066% 62,963% 0,85 0,002% 78,704% 114

6 0,90 0,000% 94,577% 0,95 0,000% 100,000% R. M. Moreo; A. Gozaga Fig. 6. False accept probability, P (FA) ad false reject probability, P (FR). The ROC curve, which represets the system accuracy, is showed i fig. 7 ad was built with the P (FA) e P (FR) values showed i figure 6. Coclusio Fig. 7. ROC curve. The ROC curve aalysis validates the Haralick s features for usig as a biometric feature extractio of huma beig, because they ca reproduce the iris uique feature. Also, it is possible to coclude that the way chose to divide the iris rig is a efficiet method to obtai a uiform texture regio. Aother importat poit is that i the majority of the cases, the false accepted ad the false rejected were obtaied due to some kid of fail i the iris image. The partial occlusio ad the lack of focus were the pricipal fail reasos. Fig. 8 shows a eye image with the iris very obstructe what turs its idetificatio a hard job. 115

7 Fig. 8. Partial occlusio of the iris. The idetificatio also becomes difficult whe the images, used to build the mea feature vector, have may differeces. Refereces [1] Negi, M. et al. (2000). A iris biometric system for public ad persoal use. IEEE, p [2] Jai, A.K. et al. (1999). Persoal Idetificatio i a etwork society. Norwell, MA - Kluwer. [3] Adler, F.H. (1965). Physiology of the eye: Cliical applicatio. The C. V. Mosby Compay, 4a edição, Lodres. [4] Daugma, J. (1993). High cofidece visual recogitio of persos by a test of statistical idepedece. IEEE Trasactios o Patter Aalysis ad Machie Itelligece, v.15, o. 11, p [5] El-Balkry, H.M. (2001). Huma iris detectio usig fast cooperative modular eural ets. Neural works, Proceedigs of Iteratioal Joit Coferece o IJCNN 01, v.1, p [6] Willias, G.O. (1997). Iris recogitio techology. IEEE AES Systems Magazie, abril 1997, p [7] Haralick, R.M.; Shamuga, M.K. (1973). Computer classificatio of reservoir sadstoes. IEEE Trasactios o Geosciece Electroics, v.11, o. 4, p , Oct. [8] Hough, P.V.C. (1962). Methods ad meas for recogizig complex patters. U.S. Patet [9] CASIA. Iris Image Database versão 1.0, 116