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High Security Human Recognition System using Iris Images

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In this paper, efficient biometric security …

In this paper, efficient biometric security
technique for Integer Wavelet Transform based Human
Recognition System (IWTHRS) using Iris images
verification is described. Human Recognition using Iris
images is one of the most secure and authentic among the
other biometrics. The Iris and Pupil boundaries of an Eye
are identified by Integro-Differential Operator. The features
of the normalized Iris are extracted using Integer Wavelet
Transform and Discrete Wavelet Transform. The Hamming
Distance is used for matching of two Iris feature vectors. It
is observed that the values of FAR, FRR, EER and
computation time required are improved in the case of
Integer Wavelet Transform based Human Recognition
System as compared to Discrete Wavelet Transform based
Human Recognition System (DWTHRS).

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  • 1. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 High Security Human Recognition System using Iris Images C. R. Prashanth1, Shashikumar D.R.2, K. B. Raja3, K. R. Venugopal3, L. M. Patnaik4 1 Department of Electronics and Communication Engineering, Vemana Institute of Technology, Bangalore, India 2 Department of Computer Science and Engineering, Cambridge Institute of Technology, Bangalore, India 3 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore 560 001, India 4 Vice Chancellor, Defence Institute of Advanced Technology, Pune, India prashanthcr_ujjani@yahoo.comAbstract—In this paper, efficient biometric security after two or three years. (ii) The human Iris might be astechnique for Integer Wavelet Transform based Human distinct as the Finger Prints for the different individuals.Recognition System (IWTHRS) using Iris images (iii) The forming of Iris depends on the initialverification is described. Human Recognition using Iris environment of the Embryo and hence the Iris Textureimages is one of the most secure and authentic among the Pattern does not correlate with genetic determination. (iv)other biometrics. The Iris and Pupil boundaries of an Eyeare identified by Integro-Differential Operator. The features Even the left and the right Irises of the same person areof the normalized Iris are extracted using Integer Wavelet unique. (v) It is almost impossible to modify the IrisTransform and Discrete Wavelet Transform. The Hamming structure by surgery. (vi) The Iris Recognition is non-Distance is used for matching of two Iris feature vectors. It invasive. (vii) It has about 245 degrees of freedom.is observed that the values of FAR, FRR, EER and Iris is the only internal organ which can be seencomputation time required are improved in the case of outside the body. The probability of uniqueness amongInteger Wavelet Transform based Human Recognition all humans has made Iris Recognition a reliable andSystem as compared to Discrete Wavelet Transform based efficient Human Recognition Technique. An IrisHuman Recognition System (DWTHRS). biometric system can be utilized in two contexts: verification and identification. Verification is a one-to-Index Terms—Human Recognition, Biometrics, IntegerWavelet Transforms, Iris Image, High Security. one match in which the biometric system tries to verify a person’s identity by comparing the distance between test Iris and the corresponding Iris in the database, with a I. INTRODUCTION predefined threshold. If the computed distance is smaller Biometric solutions address the security issues than the predefined threshold, the subject is accepted asassociated with traditional method of Human Recognition being genuine, else the subject is rejected. Identificationbased on personal identification number (PIN), identity is a one-to-many match in which the system compares thecard, secrete password etc., and the traditional methods test Iris with all the Irises in the database and chooses theface severe problems such as loss of identity cards and sample with the minimum computed distance i e.,forgetting/ guessing the passwords. Biometric measures greatest similarity as the identified result. If the test Irisbased on physiological or behavioral characteristics are and the selected database Iris are from the same subject, itunique to an individual and have the ability to reliably is a correct match. The term authentication is often useddistinguish between genuine person and an imposter. The as a synonym for verification.physiological characteristics include Iris, Finger Print, The Iris Verification system can be split into fourRetinal, Palm Prints, Hand Geometry, Ear, Face and stages: data acquisition, segmentation, encoding andDNA, while the behavioral characteristics include matching. The data acquisition step captures the IrisHandwriting, Signature, Body Odor, Gait, Gesture and images using Infra-Red (IR) illumination. The IrisThermal Emission of Human Body. Segmentation step localizes the Iris region in the image. The biometric systems based on behavioral For most algorithms and assuming near-frontalcharacteristics fail in many cases as the characteristics presentation of the Pupil, the Iris boundaries are modeledcan easily be learnt and changed by practice. Some of the as two circles, which are not necessarily concentric. Thetechniques based on physiological characteristics such as inner circle is the pupillary boundary between the PupilFace Recognition, Finger Prints and Hand Geometry also and the Iris whereas the outer circle is the limbicfail when used over a long time as they may change due boundary between the Iris and the Sclera. The noise dueto ageing or cuts and burns. Among all the biometric to Eyelid occlusions, Eyelash occlusions, Speculartechniques Iris Recognition has drawn a lot of interest in highlights and Shadows are eliminated usingPattern Recognition and Machine Learning research area segmentation. Most segmentation algorithms are gradientbecause of the advantages viz., (i) The Iris formation based that is segmentation is performed by finding thestarts in the third month of gestation period and is largely Pupil-Iris edge and the Iris-Sclera edge. The encodingcomplete by the eighth month and then it does not change stage encodes the Iris image texture into a bit vector code. 26© 2010 ACEEEDOI: 01.ijsip.01.01.06
  • 2. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010The corresponding matching stage calculates the distance Chin et al., [5] proposed the use of an S-Iris encodingbetween Iris codes, and decides whether it is a match in which is generated from the inner product of the outputthe verification context or recognizes the submitted Iris from a 1D Log Gabor filter and secret pseudorandomfrom the subjects in the database. Biometrics is widely numbers. In the segmentation stage, first an edge map isused in many applications such as access control to generated using a Canny edge detector. A Circular Houghsecure facilities, verification of financial transactions, Transform is used to obtain the Iris boundaries. Linearwelfare fraud protection, law enforcement, and Hough Transform is used in excluding the Eyelid andimmigration status checking when entering a country. Eyelash noises. The isolated Iris part is unwrapped into a Contribution: In this paper, we propose a novel rectangle with a resolution of 20 * 240 using Daugman’stechnique for human identity authentication by Iris rubber sheet model. In matching, Hamming Distance isVerification. We use Integro-Differential equation for Iris used to indicate the dissimilarity between a pair of Irislocalization and Daugman’s rubber-sheet model for codes.normalization. Integer Wavelet Transformation is used to Ya-Ping Huang et al., [6] proposed a recognitionextract the features from the normalized Iris image. method which constructs basic functions for training setMatching between the test image and the database images by Independent Component Analysis, which determinesis done using Hamming Distance. the centre of each class by competitive learning Organization of the paper: The rest of the paper is mechanism and finally recognizes the pattern based onorganized as follows. In section II, we discuss about Euclidean Distance. No restriction for image captureliterature survey. In section III we present the Iris based owing to representation of size and rotation invariance.Human Recognition model. In section IV we discuss the However, the algorithm uses all patterns of each class asIWTHRS algorithm. The performance analysis presented a whole to estimate ICA basic function and when a newin section V and concluded in section VI. class is added all the patterns must be trained again. Schmid et al., [7] proposed an algorithm to predict the II. LITERATURE SURVEY Iris Biometrics system performance on a larger dataset based on the Gaussian Model constructed from a smaller Daugman’s Algorithm [1, 2] proposed the Iris model dataset. In the matching stage, it uses a sequence of K Irisas two circles between the Pupil and Sclera boundaries, codes to represent an Iris subject. The distance between awhich are not necessarily concentric. Each circle is pair of Iris subjects is defined as a K-dimensionaldefined by three parameters (xo, yo, r), where (xo, yo) Hamming Distance, modeled as Gaussian Distribution.locates the center of the circle of radius r. An Integro- Fancourt et al., [8] discussed the problem of IrisDifferential Operator is used to estimate the three Recognition using images acquired up to 10 meters away.parameter values for each circular boundary. The The pictures are captured with the aid of a telescope. Thesegmented Iris image is normalized and converted from manual Iris segmentation is used as a bootstrap to theCartesian image coordinates to polar image coordinates. automatic segmentation. The similarity between theThe 2D Gabor filter is used to encode the Iris image to a gallery image and probe image is measured by thebinary code of 256 bytes in length. Hamming Distance is average correlation coefficient over sub-blocks with aused to verify the similarity of two Iris codes. size of 12*12 pixels. The algorithm is tested on two iris In an algorithm proposed by Ma et al., [3], the Iris databases with no subjects in common.images are projected to the vertical and horizontaldirections to estimate the center of the Pupil, to save time III. IWTHRS MODELin searching for the Iris boundaries. The region of Iris isconstrained close to the Pupil, because Iris texture is In this section, IWTHRS model is discussed. Figure 1claimed to be more abundant and also it reduces Eyelid shows the block diagram of Integer Wavelet Transformand Eyelash noise. The representation of the Iris is a based Human Recognition System (IWTHRS), whichfeature vector of length 1,536 bits. A Fisher Linear verifies the authenticity of given Iris of a person. The EyeDiscriminant is used to reduce the dimension of the Iris images for study are taken from the CASIA database. Thefeature vector. Integro-Differential Operator (IDO) is used for Iris Kong and Zhang [4] proposed an Eyelash and localization and Daugman’s rubber-sheet model forreflection segmentation in their algorithm. The Iris normalization. Integer Wavelet Transformation is used tosegmentation is implemented by using curve fitting extract the features from the normalized Iris image.approaches. The Eyelashes are sub-classified as separable Matching between the test Iris and the database Irises isEyelashes and multiple Eyelashes. The separable done using Hamming Distance.Eyelashes are segmented using a Gabor filter and the A. Integro-Differential Operator for Image Segmentationmultiple Eyelashes are segmented by comparing thevariance of intensity values of a given area with the The Integro-Differential Operator is defined by thepredefined threshold. Four types of 1-D wavelets viz., Equation 1.Mexican hat, Haar, Shannon and Gabor are used to ∂ I ( x, y ) max (r , xo , yo ) = Gσ (r ) ∗extract the Iris features. In matching, the dissimilaritybetween a pair of Iris codes is defined by L1 norm. ∫, y 2πr ds ∂r r , x0 0 (1) 27© 2010 ACEEEDOI: 01.ijsip.01.01.06
  • 3. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 Where I(x,y) is the Eye image, r is the radius, Gσ(r) is from 0 to 1 and θ is angle in the interval from 0 to 2 π .a Gaussian smoothing function, and s is the contour of The remapping of the Iris region from ( x, y ) Cartesianthe circle given by (r, x0, y0). The operator searches for coordinates to the normalized non-concentric polarthe circular path where there is maximum change in pixel representation is modeled as given by the Equations 2, 3values, by varying the radius and centre x and y position and 4.of the circular contour. I ( x(r ,θ ), y (r ,θ )) = I (r ,θ ) (2) Eye Image With x(r ,θ ) = (1 − r )x p (θ ) + rxl (θ ) (3) Integro-Differential Operator y (r ,θ ) = (1 − r ) y p (θ ) + ryl (θ ) (4) Daugman’s Rubber sheet model where I ( x, y ) is the Iris image, ( x, y ) are the original Cartesian coordinates, (r ,θ ) are the corresponding normalized polar coordinates, and are the coordinates of Image Enhancement the pupil and iris boundaries along the θ direction as shown in Figure 3. Feature Extraction using IWT Database Hamming Distance Figure 3. Daugman’s Rubber Sheet Model Verified Iris The rubber sheet model takes into account Pupil dilation and size inconsistencies in order to produce a Figure 1. Block diagram of IWTHRS normalized representation with constant dimensions. The Iris region is modeled as a flexible rubber sheet anchored The IDO is applied iteratively with the amount of at the Iris boundary with the Pupil centre as the referencesmoothing progressively reduced in order to attain precise point. The segmented Iris image is normalized to a sizelocalization and also Eyelids are localized with the path 60 * 250.of contour integration changed from circular to an arc.The Integro-Differential can be seen as a variation of the C. Image EnhancementHough Transform, as it makes use of first derivatives of In order to obtain best features for Iris verification,the image and performs a search to find geometric polar transformed image is enhanced using contrast-parameters. The IDO works with raw derivative limited adaptive histogram equalization [9]. The resultsinformation and hence it does not suffer from the of image before and after enhancement are shown inthreshold problems of Hough Transform. The segmented Figure 4.Iris image is shown in Figure 2. (a) (b) Figure 4. (a) Normalized Iris before enhancement. Figure 2. Segmented Iris with occluding Eyelids and Eyelashes made (b) Normalized Iris after enhancement. black D. Feature ExtractionB. Daugman’s Rubber Sheet Model Feature extraction is the most important step in Iris The homogenous rubber sheet model devised by Verification. We use Haar Integer WaveletDaugman remaps each point within the Iris region to a Transformation to extract the features from thepair of polar coordinates (r ,θ ) where r is in the interval normalized Iris image. The normalized Iris image of size 28© 2010 ACEEEDOI: 01.ijsip.01.01.06
  • 4. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 201060*250 is subjected to Integer Wavelet Transformation to totally different. If two patterns are derived from theget Approximation band, Horizontal band, Vertical band same Iris, the Hamming Distance between them is closeand Diagonal band. The Horizontal Detail band obtained to zero, since they are highly correlated and the bitsafter the first level Integer Wavelet Transformation is should agree between the two Iris codes. However,further subjected to two levels of decomposition. The because of the presence of noise due to Eyelid andapproximation band obtained after the third level Eyelashes occlusion, the Hamming Distance may vary updecomposition consists of the prominent features. The to 0.4 even for the same Iris images captured at differenthorizontal band is selected at the first two stages of instances. To increase the efficiency, we compare the Irisdecomposition, because the normalized Iris image shows image under test with all the 7 images of each group andmore details in the horizontal direction i e., angular the mean value of the 7 Hamming Distances is used todimensions of the actual Iris image compared to the decide whether the Iris image under test belongs to thevertical direction i e., the radial dimension of the actual same group or not. If the average Hamming DistanceIris image. The two dimensional approximation band obtained is greater than 0.39 then the subject is rejectedcontaining the prominent features is converted into a one and if the average Hamming Distance is lesser than 0.39dimensional array and it is binarized. To binarize, we then the subject is accepted as genuine.equate all the positive features to 1 and the negativefeatures to 0. This finally results in a feature vector of IV. ALGORITHMsize 256 bits. The conceptual model for the three levels Table 1 shows the Human Identification by IWTHRSInteger Wavelet decomposition for feature extraction is algorithm in which the authenticity of the test Iris imageshown in Figure 5. is verified. Problem definition: LL LL HL Consider an Eye image of a subject whose identity has to LH HH be verified. The objective is to LL i) Segment the Iris with minimum noise, ii) Normalize the Iris, iii) Generate a minimum length feature vector, which LH HH includes all the distinct features of the Iris, and iv) verify the authenticity of the subject. Assumptions: LH HH i) The Eye image is captured using IR photography ii) The Eye image is a gray-scale image of size 150* 200 TABLE 1. IWTHRS ALGORITHM Input : Test Eye image. Figure 5. Conceptual diagram for 3 levels 2D Integer Wavelet Decomposition. Output: Verified Iris.E. Matching i. Segment the Iris image using IDO ii. Normalize the segmented Iris image from Matching between the two Iris feature vectors is done Cartesian coordinates to the normalizedusing Hamming Distance. It is a measure of how many non-concentric polar representation of sizebits are the same between two bit patterns. Using the 60*250 using Daugman’s rubber sheetHamming Distance of two bit patterns, a decision is made modelas to whether the two patterns were generated from iii. Enhance the image using contrast limiteddifferent Irises or from the same one. In comparing the bit adaptive histogram equalizationpatterns X and Y, the Hamming Distance HD, is defined iv. Apply Integer Wavelet Transformation toas the sum of disagreeing bits over N, the total number of the normalized Iris imagebits in the feature vectors and is given by the Equation 5. v. Subject the horizontal detail band obtained 1 N in step 4 to two level IWT HD = ∑ X j ⊕ Yj N j =1 (5) vi. Convert the approximation band obtained in step 5 into single dimension vii. Binarize the one dimensional array Since an individual Iris region contains features with viii. Find the Hamming Distance between thehigh degrees of freedom, each Iris region produces a bit- binarized feature vectors obtained in step7pattern which is independent to that produced by another with the corresponding feature vector inIris. On the other hand, two Iris codes produced from the the databasesame Iris will be highly correlated. In ideal case, if two ix. If HD<0.39, the subject is accepted asbits patterns are completely independent, such as Iris genuine, else rejectedtemplates generated from different Irises, the HammingDistance between the two patterns is high. This occursbecause independence implies the two bit patterns will be 29© 2010 ACEEEDOI: 01.ijsip.01.01.06
  • 5. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 V. PERFORMANCE ANALYSIS We tested the IWTHRS model on the CASIA Irisimage database–version 1.0, which contains 756 gray-scale Eye images with 108 unique Eyes or classes and 7different images of each unique Eye. The algorithm issimulated on MATLAB 7.4 version. For the performanceanalysis, we considered 200 gray-scale Iris images out ofavailable 756 Iris images. Table 2 gives the value ofFAR, FRR and EER obtained with Hamming Distancethreshold of 0.39 for IWT and DWT. It is observed thatthe value of FAR, FRR and EER are less in the case ofIris verification by IWTHRS compared that by Figure 6. Graph of FAR and FRR for DWTHRSDWTHRS.TABLE 2. COMPARISON OF FAR, FRR AND EER VALUES FOR IWTHRS AND DWTHRS. IWTHRS DWTHRS FAR 0.11 0.19 FRR 0.105 0.135 EER 0.107 0.165 Table 3 gives the average computation time fordifferent steps viz., Segmentation, Normalization,Enhancement, Feature extraction and Matching involvedin IWTHRS and DWTHRS. It is observed that the time Figure 7. Graph of FAR and FRR for IWTHRSrequired for feature extraction in case of IWTHRS is only0.16 ms when compared to 0.49 ms for DWTHRS. Thus VI. CONCLUSIONIWTHRS reduces the computation time for featureextraction by 66%. This shows that the proposed system A novel technique for Human Recognition using Iriscan perform better in real time. verification has been proposed. The scheme uses the Integer Wavelet Transformation on the normalized Iris TABLE 3. AVERAGE COMPUTATION TIME OF THE IWTHRS AND image to extract the distinct features of the Iris. The DWTHRS MODELS. implementation of IWTHRS in place of DWTHRS has remarkably improved the computation speed and IWTHRS DWTHRS efficiency. Matching the test image with a set of seven Time % of Time % of images instead of only one image and finding the mean of (ms) total (ms) total the Hamming Distances for decision making further time time improves the efficiency of the algorithm. Improvements Segmentation 12.92 94.7 12.92 92.48 can further be made by including Iris de-noising 2 techniques into the algorithm. Normalization 0.39 2.88 0.39 2.79 Enhancement 0.08 0.58 0.08 0.57 ACKNOWLEDGMENT Feature 0.16 1.17 0.49 3.50 1 Extraction The author is thankful to KRJS management, the Matching 0.09 0.65 0.09 0.064 Principal, Vemana Institute of Technology, and the Total Time 13.64 100 13.97 100 Principal, UVCE for providing the infrastructural facilities to carry out the research work. Figures 6 and 7 show the graph of FAR and FRRobtained for different values of Hamming Distance REFERENCESthreshold to compare the performance of Iris based [1] J .Daugman, “High Confidence Visual Recognition ofHuman Recognition System using DWT and IWT for Persons by a Test of Statistical Independence,” IEEEfeature extraction. As Hamming Distance increases, the Transactions on Pattern Analysis and Machinevalue of FRR decreases whereas FAR increases. The Intelligence, vol.15, no. 11, pp. 1148–1161, Novembervalue of EER is a point where FAR is equal to FRR. 1993. [2] J. Daugman, “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns,” International Journal of Computer Vision, vol. 45, no. 1, pp. 25–38, 2001. [3] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal Identification based on Iris Texture Analysis,” IEEE 30© 2010 ACEEEDOI: 01.ijsip.01.01.06
  • 6. ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 Transactions on Pattern Analysis and Machine Bangalore. He obtained his BE and ME in Electronics and Intelligence, vol. 25, no. 12, pp. 1519–1533, December Communication Engineering from University Visvesvaraya 2003. College of Engineering, Bangalore. He was awarded Ph.D. in[4] W. Kong and D. Zhang, “Detecting Eyelash and Reflection Computer Science and Engineering from Bangalore University. for Accurate Iris Segmentation,” International Journal of He has over 35 research publications in refereed International Pattern Recognition and Artificial Intelligence, pp. 1025– Journals and Conference Proceedings. His research interests 1034, 2003. include Image Processing, Biometrics, VLSI Signal Processing,[5] C. Chin, A. Jin, and D. Ling, “High Security Iris computer networks. Verification System based on Random Secret Integration,” Proceedings of International conference on Computer Vision and Image Understanding, vol. 2, pp. 169-177, May 2005. K R Venugopal is currently the Principal and Dean, Faculty of[6] Ya-Ping Huang, Si-Wel Luo and En-Yi Chen, “An Engineering, University Visvesvaraya College of Engineering, Efficient Iris Recognition System,” Proceedings of the Bangalore University, Bangalore. He obtained his Bachelor of First International Conference on Machine Learning and Engineering from University Visvesvaraya College of Cybernetics, pp. 450-454, November 2002. Engineering. He received his Masters[7] N. Schmid, M. Ketkar, H. Singh, and B. Cukic, degree in Computer Science and “Performance Analysis of Iris-based Identification System Automation from Indian Institute of at the Matching Score Level,” IEEE Transactions on Science, Bangalore. He was awarded Information Forensics and Security, vol. 1, no. 2, pp. 154- Ph.D. in Economics from Bangalore 168, 2006. University and Ph.D. in Computer Science[8] C. Fancourt, L. Bogoni, K. Hanna, Y. Guo, R. Wildes, N. from Indian Institute of Technology, Takahashi, and U. Jain, “Iris Recognition at a Distance,” Madras. He has a distinguished academic career and has degrees Proceedings of International Conference on Audio and in Electronics, Economics, Law, Business Finance, Public Video based Biometric Person Authentication, pp. 1–13, Relations, Communications, Industrial Relations, Computer 2005. Science and Journalism. He has authored 27 books on Computer[9] Karel Zuiderveld, “Contrast Limited Adaptive Histogram Science and Economics, which include Petrodollar and the Equalization,” Proceedings of the First International World Economy, C Aptitude, Mastering C, Microprocessor Conference on Visualization in Biomedical Computing, pp. Programming, Mastering C++ etc. He has been serving as the 337-345, May 1990. Professor and Chairman, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Prashanth C R received the BE degree Bangalore University, Bangalore. During his three decades of in Electronics and the ME degree in service at UVCE he has over 200 research papers to his credit. Digital Communication from His research interests include computer networks, parallel and Bangalore University, Bangalore. He distributed systems, digital signal processing and data mining. is pursuing his Ph.D. in Computer Science and Engineering of Bangalore L M Patnaik is the Vice Chancellor, Defence Institute of University under the guidance of Dr. Advanced Technology (DeemedK. B. Raja, Assistant Professor, Department of Electronics and University), Pune, India. During the pastCommunication Engineering, University Visvesvaraya College 35 years of his service at the Indianof Engineering. He is currently an Assistant Professor, Dept. of Institute of Science, Bangalore, He hasElectronics and Communication Engineering, Vemana Institute over 500 research publications in refereedof Technology, Bangalore. His research interests include International Journals and ConferenceComputer Vision, Pattern Recognition, Biometrics, and Proceedings. He is a Fellow of all the fourCommunication Engineering. He is a life member of Indian leading Science and EngineeringSociety for Technical Education, New Delhi. Academies in India; Fellow of the IEEE and the Academy of Science for the Developing World. He has received twenty Shashikumar D R is a Professor in the national and international awards; notable among them is the department of Computer Science and IEEE Technical Achievement Award for his significant Engineering, Cambridge Institute of contributions to high performance computing and soft Technology, Bangalore. He obtained his computing. His areas of research interest have been parallel and B.E. Degree in Electronics and distributed computing, mobile computing, CAD for VLSI Communications Engineering from circuits, soft computing, and computational neuroscience. Mysore University, Mysore and Mastersdegree in Electronics from Bangalore University, Bangalore. Heis pursuing research in the area of Biometric applications. Hisarea of interest is in the field of Digital Image Processing,Microprocessors, Embedded systems, Networks and Biometrics.He is life member of Indian Society for Technical Education,New Delhi. K B Raja is an Assistant Professor, Dept. of Electronics and Communication Engineering, University Visvesvaraya college of Engineering, Bangalore University, 31© 2010 ACEEEDOI: 01.ijsip.01.01.06