2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 24-27 November 2007, Dubai, United Arab Emirates

FEATURE EXTRACTION OF IRIS IMAGES USING ICAFOR PERSON AUTHENTICATION

'SanjayN Talbar, 2Rajesh MBodade

'S.G.G.S. Institute of Engineering and Technology, Nanded-431606, India. snta1bargyahoo.com2Military College of Telecommunication Engineering, Mhow-45344 1, India. raieshbodade(gmailcom

ABSTRACT

This paper describes the Iris based authentication systemusing the Independent Component Analysis (ICA). Inthis paper, a customized iris extraction algorithm hasbeen applied directly on the unfiltered grayscale image.The polar coordinate system has been used by theresearchers for iris processing. Instead, an algorithmbased on intrinsic coordinate system has been proposedto save on computation with satisfactory results. ICA hasbeen used very successfully in face recognition. Theproposed algorithm of ICA has been tested on "UBIRISIris Database of noisy and noise free images" of averageiris visibility and the results showed the accuracy ofidentification more than 83% with excellent processingspeed.

Index Terms- Biometrics Identification, IrisAuthentication, ICA, Iris localization, noisy iris.

1. INTRODUCTION

With the advances in the need of confirming identity of thepeople in various establishments, there has been a growingneed of providing better security of information and alsorecognition of individual human beings in differing scenariosand not only where security alone is not the lone necessity[1]-[6]. The iris recognition till now had been developed upon thetheorems like Gabor filters, Wavelet Transform, PrincipalComponent Analysis etc by many reseachers [7]-[16].

Gabor filter and Wavelet Transform based algorithms arecomparatively computationally heavy. PCA is a key techniquein the analysis of biometric data i.e. multivariate data,encompassing three possible objectives: Description,Interpretation and Modeling of data. Orthogonality is a centralplank in all three aspects but this technique has a number ofdrawbacks as regards each of the three objectives [17]-[19].

Independence Component Analysis (ICA) is a signal processingtechnique whose goal is to express a set of random variables aslinear combinations of statistically independent componentvariables. Order these extracted sequences through a certaincriteria, one can find the main influential factors from the firstseveral ones. PCA uses up to second order moment of the datato produce uncorrelated components, whereas ICA strives togenerate components as independent as possible. Thus, ICA

acts as a tool for Blind Source Separation and Signalreconstruction[1 7].

A primary goal in pattern recognition, then, is to find someintrinsic coordinate system in which the data structure is mostapparent. With the increase of computational power over recentyears, however, the use of higher-order Information has becomefeasible leading to more sophisticated non-Gaussian methodsi.e. ICA. Hence, novel algorithm of iris extraction and featureextraction algorithm using ICA is proposed. The proposedalgorithm has been tested successfully for iris authenticationsystem and results showed encouraging success rate at excellentprocessing speed.

2. ICA FOR IRIS AUTHENTICATION

The most common application of ICA in biometrics is the Facerecognition. In iris authentication, as in how the promising ICAtechnique extracts features that are more closely related to ourintuition of discriminate information, and that improve thesuccess rate compared to an equivalent system using PCA.

ICA is a data analysis tool derived from the "source separation"signal processing techniques. The aim of source separation is torecover original signals Si, from known observations Xj, whereeach observation is an (unknown) mixture of the originalsignals. Under the assumption that the original signals Si arestatistically independent, and under mild conditions on themixture, it is possible to recover the original signals from theobservations. If the mixing is linear, ICA estimates the inverseof the mixing matrix. This view is not far from the PCAexpansion: the eigenvectors of PCA are replaced by theindependent source vectors in ICA. For a review of ICAtechniques and properties, irises in the learning set, viewed ashigh-dimensional vectors, are linear combination of unknownindependent source vectors. This may not be strictly true,depending on the respective number of images in the databaseand size of an image in pixels, but in any case ICA will findestimates of independent source vectors that are optimal toreconstruct the original images and to use the coefficients of theICA expansion as feature vectors for the irises. It is expectedthat, ICA source vectors being independent they will be closerto natural features of images, and thus more able to representdifferences between irises[17]-[24].

3. PROPOSED WORK

The flow graph with various stages of the proposed algorithmis as shown in Fig.(1).

1-4244-1236-6/07/$25.00 © 2007 IEEE 1055

3.1. Flow-graphLet R3 and R4 are some of the rows of iris image, as shown inFigure2. The raster scanning method is used to read the image.Initially it would encounter such rows, say R3, in which pixelswould be bright/white. The moment it encounters a row such as,say R4, in which it will get a dark pixel. It records this distancesay xl and moves ahead until it again encounters bright/whitepixel. It records this distance say x2. Next step is to find thedifference xl -x2 say x. The raster scans next row and performsthe same operation. In every next row the value of x will go onincreasing but at a certain row it will go up to maximum andthereafter it would start decreasing. The row, in which, value ofx is maximum, contains the center of the alleged iris. Next stepis to perform exactly the same operation for the columns. In thiscase again a column with maximum 'y' value is obtained. Thiscolumn contains the iris center. Intersection of the previouslyobtained row and this column gives the exact coordinates of theiris. Radius of iris is also calculated from this data and iris isextracted from an eye image.Another process that is carried out in this stage is digitization /binarization of the extracted iris. This process comprisesformation of a 2D matrix in which each pixel of imagecorresponds to each element of this matrix. Consider thefollowing matrix, say, A=

In this matrix the numbers in black fonts are corresponding tothe brighter/white portions of the image and the numbers inpink font are corresponding to darker/black portions of theimage. Digitization / Binarisation of the image would convertthe above obtained matrix into the following form say, B

o o 0o o 62

o O 54The above matrix is created for the complete extracted irisimage.

3.3. Processing and feature extraction

Algorithm

3.2. Preprocessing and Iris Extraction

It is primarily a segmentation which refers to the extraction ofthe iris from the eye image. Segmentation which can otherwisebe carried out by filtering, in this paper, a customized irisextraction algorithm has been applied directly on the unfilteredgrayscale image[8][10][12][14][15] [25].

.R3........................ R4

Preprocessing of an eye image

The processing refers to application of mathematical Algorithmof ICA on each extracted iris image in template creation andverification stage. It is encapsulated in following stages.3.3.1. Composite matrixformation ofeach image.(a).The Matrix B obtained in the last step of preprocessingstage is the input for this stage.(b). A counter is initialized and the matrix is scanned frombeginning till the end.(c). The counter value at the end of computational scan givesthe total number of interested pixel positions.(d).Thus the composite matrix is created in the preprocessingstage which has been used in the present sub stage3.3.2. Formation offeature vectors. f18]-f19](a). In compliance with ICA theory, this stage deals withcreation of feature vectors.(b). The complete composite matrix is to be made into a linearmatrix. A linear matrix is created with the total length given bythe counter obtained in above step.

(c). The linear matrix so obtained is now initialized with thevalues of the matrix B of the preprocessing stage.

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(d). Then, the feature vectors are created by dividing thecounter value (Iris Vector Length) by 100. Thus, linearmatrices, each of 100 pixel size are formed and these smallmatrices are termed as feature vectors.3.3.3. Computing moments of the feature vectors and Creationoftemplates.(a). This stage deals with the calculation of moments / higherorder moments of the above extracted vectors.(b). The mean, variance and covariance of the feature vectorsare computed.(d). These moments are associated with the correspondingimages so as to create a template for each authorized person andthe template is stored in the data base for the further comparisonwhen a user wants to authenticate himself.

3.4. Serialization and de-serialization

The serialization refers to storing the templates created in theprocessing stage into a permanent storage area so as to make itpossible for retrieving it at a later stage for comparison /matching purposes.

3.5. Verification/Authentication

This is the final and the actual comparing stage of thealgorithm. The term verification / authentication refers to theidentifying and confirming a person's identity by comparing hisiris image features against the previously serialized templatedatabase. The authentication is carried out by computation ofdistance between the feature vector of stored templates and thatof user image. If distance is less than threshold then person isauthenticated otherwise unauthenticated. The threshold valuefor error percentage has been decided upon the tread offbetween FAR and FRR. The threshold value is fixed to 20.

4. DATABASE OF IRIS IMAGESUBIRIS Iris Database (UBIRIS.vl) with good and noisy irisimages are used for testing the proposed algorithm[26]. Thestatistical details of the database is given Table 1.

Table 1. Statistics of Iris Image Database used for testing

Characteristics Session 1 Session 2of UBIRIS. vi

Sensor Nikon E5700 Nikon E5700

Environment Indoor Outdoor

Quality Good Noisy

No. of subjects 240 240

No. of images 240*5 =1200 240*5 = 1200

Resolution 200* 150 (Low) 200* 150 (Low)

Format Jpeg / Grayscale Jpeg / Grayscale

Controlled VaryingFeatures Illumination and Illumination and

focus blurred focus

The sample images and their noisy counterparts of the databaseis as shown in figure 3(a) and (b).

(a) Session 1: Noise (b) Session 2: NoisyFree Images ImagesFigure 3. UBIRIS vl Iris Image Database

5. RESULTS

The results showed the overall (for good and noisy images)authentication success rate of 83% with excellent processingspeed because of low resolution images. Figure 4 shows theextracted iris from eye image which is a result of proposedpreprocessing and Iris extraction algorithm.

(a) Eye (Good Quality) Image (b) Extracted Iris

Figure 4. Result (sample) of Iris Extraction Algorithm

The length of the initial linear vector is 6382. This is dividedinto 64 linear matrices of 100 pixel each. Table 2 shows theerror distance of template from input image.

Table 2. Error Distance of Iris of Figure 3(b)With Unauthenticated Iris With Authenticated Iris

22.34 17.84

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Table 3 shows the FAR and FRR for different values ofthreshold.

Table 3. FAR and FRR in % for different thresholdsT Good Images Noisy Images

Threshold FAR FRR FAR FRR22.0 73.358 0.000 87.934 0.00021.5 38.175 0.000 44.667 0.00021.0 5.356 0.000 9.538 0.00020.5 0.094 0.000 1.756 0.11220.0 0.000 0.000 0.014 2.40319.5 0.000 0.784 0.003 9.43719.0 0.000 11.377 0.000 15.98318.5 0.000 38.338 0.000 45.00818.0 0.000 63.334 0.000 78.02617.5 0.000 77.668 0.000 89.277

6. CONCLUSIONS AND FUTURE SCOPEBased on above stated results of the algorithm, followingconclusions are drawn.(i). The various researchers have proposed polar coordinatesfor iris recognition but in this work, the intrinsic coordinatesystem has been used to save on computation with satisfactoryresults.(ii). Iris is extracted, successfully, directly from noisy eyeimage without any noise reduction technique. Hence,preprocessing time is saved in the proposed algorithm.(iii). The ICA will prove to be the main technique for doing theiris scans as the ICA technique does away with the Eigenvectors concept of PCA and introduces the source vectorconcept, where in the non correlation is not the only criterionbut the independence of sources also plays an important role.(iv). Because of use ofICA (source vector concept) for Iris (lowbiometric data processing), accuracy of authentication at highprocessing speed, as compared face, is achieved.(v). The proposed algorithm has been tested on low resolutiongrayscale, noise free and noisy, (with varying illumination,defocused and blurred) iris images of "UBIRIS vl IrisDatabase" for authentication of a person and overall successrate of 83% at excellent processing speed is achieved.(vi). With noise-free and noisy image datasets over 92 % andnear 75%, respectively, iris have been extracted successfully.(vii). Both FAR and FRR for noise free image dataset isachieved 0.0% for the threshold of 20.0. For noisy imagedataset FAR is 0.014 % and FRR is 2.403 % for a threshold of20.0, which allows for best authentication rate.(vii). Proposed algorithm can be modified for "Bimodal /Multimodal Biometric Recognition System usingiris/face/speech signals.

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