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1 | International Journal of Computer Systems, ISSN-(2394-1065), Vol. 03, Issue 01, January, 2016

International Journal of Computer Systems (ISSN: 2394-1065), Volume 03 – Issue 01, January, 2016

Available at http://www.ijcsonline.com/

New Improved Feature Extraction Approach of IRIS Recognition

Jyoti pooniaA, Parvati Bhurani

A, Sandeep Kumar Gupta

B, Shubh Lakshmi Agrwal

C

AGovernment Mahila Engineering College, AjmerBJECRC University, Jaipur, India

C

The ICFAI University, Jaipur, India

Abstract

Iris recognition is used for identify the person by recognizing iris pattern of eye of a person. Currently there is no method

of IRIS recognition system that have 100% recognition rate using Gabor filter. So research issues are to improve

recognition rate by improving the pre-processing of datasets, improving the feature extraction method and using the best

classifier for iris recognition. Feature extraction is the key step on which recognition rate depends for iris recognition.

Gabor filter extract the edge information of iris pattern using the projection on iris image but have a problem of huge

dimension and high redundancy. In the proposed technique, the dimension and redundancy is reduced effectively in

order to increases accuracy.

Keywords: IRIS Recognition, Gabor Filter, DWT, DCT.

I. I NTRODUCTION

The iris recognition is better than other identificationtechnique due to not change over the years and age in iris

pattern. The iris recognition includes iris image preprocessing, feature extraction and classification.Wavelet transform and hybrid wavelet transformsconcluded that hybrid transforms are better than simpletransform wavelet transform for better accuracy. Local irisfeatures is used for iris recognition but Man Zhang [1] et.

al. Proposed both geometric and photometric featuresextraction technique in order to get a better result. They

proposed that iris image should be decomposed intolowpass and band pass components based on non subsampled contoured transform ant then the geometricfeatures were extracted from bandpass component andordinal measure of local iris region from lowpasscomponents. R. Rizal Isnanto [2] extracted the imagefeature based on energy after the Wavelet transforms usingHaar and Daubechies and concluded that higherrecognition rate is achieved using Haar compared toDaubechies wavelet transform. R. Rizal Isnanto [3]suggested that textural characteristics of the iris pattern can

be extracted for unique identification. They used wavelettransform feature extraction technique for extracting theinformation from iris image. K. Nguyen et. al. [4]

presented feature-domain super resolution framework forGabor-based face and iris recognition. They used superresolution to improve the resolution and the recognition

performance and suggested that Current existing feature-domain super resolution approaches are limited to simplelinear features such as Principal ComponentAnalysis(PCA) and Linear Discriminant Analysis(LDA),which are not the most Discriminant features for

biometrics. Priya et. al [5], presented a new improvedfeature extraction technique based on average Gabor scale.They suggested two stage reduction technique of feature

extraction

II. RELATED WORK

A. Gabor Filter Feature Extraction

In spatial domain, Gabor filter is given by equation (1)[6].

(1)

Where (x, y), the pixel location in the digital image andSx, Sy Standard deviation in the x & y directionsrespectively. λ is inverse of central frequency and θ is angleapplied in the Gabor filter equation. The parameters x1 andy1 are given as equation (2).

x1=xcosθ + ysinθ y1= -xcosθ + ysinθ (2)

The Gabor features are evaluated using convolutionoperation of Gabor filter bank Ψ (x,y) with input image I(x, y) which is defined in equation (3).

Gu,v(x,y) = I (x,y) * Ψ (x,y) (3)

The Gabor filter bank Gu,v(x,y) is complex number so aconvolution operation of Gabor filter is performedseparately for real and imaginary part as defined inequation 4 and 5.

Re(O(x,y))m ,n = I ( x, y ) * Re(ψ(x,y,λ m,θn))(4)

Im(O(x,y))m ,n = I ( x, y ) * Im(ψ(x,y,λ m,θn))(5)

The final amplitude of Gabor filter bank is calculated asequation 6[7].

|O(x,y)|m,n=((Re(O(x,y))m ,n)2+ (Im(O(x,y))m ,n)2)1/2

(6)

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Jyoti poonia et al New Improved Feature Extraction Approach of IRIS Recognition


B. Discrete Wavelet Transformation

The term wavelet transform is explained asdecomposition of the data or the image into waveletcoefficients, comparing the detail coefficients with a given

threshold value, and shrinking these coefficients close tozero to take away the effect of noise in the data. The imageis reconstructed from the modified coefficient which isknown as the inverse discrete wavelet transforms [8]. DWTtransformation converts the iris image into four differentfrequency sub band as LL, LH, HL and HH as figure 1.Where range of frequency is represented as LL

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R EFERENCES

[1] Zhang, Man, Zhenan Sun, and Tieniu Tan. "Deformed irisrecognition using band pass geometric features and low pass ordinalfeatures", in International Conference on Biometrics (ICB), IEEE,2013.

[2] Isnanto, R. R.,” Iris recognition analysis using biorthogonal

wavelets transform for feature extraction”, In 1st InternationalConference on Information Technology, Computer and ElectricalEngineering (ICITACEE), pp. 183-187, IEEE, 2014.

[3] Isnanto, R. R., Satoto, K. I., & Windasari, I. P. Constructing irislet,“A new wavelet type which matched for iris image characteristics”,In International Conference on Information and CommunicationTechnology (ICoICT), 2nd International Conference on pp. 232-237). IEEE, 2014.

[4] Nguyen, K., Sridharan, S., Denman, S., & Fookes, C. , “Feature-domain super-resolution framework for Gabor-based face and irisrecognition”, In IEEE Conference on Computer Vision and PatternRecognition (CVPR), pp. 2642-2649, IEEE, 2012.

[5] Dosodia, P., Poonia, A., Gupta, S. K., & Agrwal, S. L.,”NewGabor-DCT Feature Extraction Technique for Facial ExpressionRecognition”, In Fifth International Conference on CommunicationSystems and Network Technologies (CSNT), pp. 546-549, IEEE,

2015.[6] George, A. M., & Durai, C. A. D., “A survey on prominent iris

recognition systems”, International Conference on InformationCommunication and Embedded Systems (ICICES), pp. 191-195,IEEE, 2013.

[7] Sandee p, Shubhlakshmi, Yogesh, Neeta, “A Hybrid method offFeature Extraction for Facial Expression Recognition” , in nseventh international conference on Signal image Technology andInternet based systems (SITIS), page(s): 422 – 425, IEEE, 2011.

[8] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient iris recognition by characterizing key local variations,” IEEE Trans on ImageProcess., vol. 13, no. 6, pp. 739 – 750,IEEE, 2004.

[9] Sharma, V. P., Mishra, S. K., & Dubey, D., “Improved IrisRecognition System Using Wavelet Transform and Ant ColonyOptimization”, In 5th International Conference on Computational

Intelligence and Communication Networks (CICN), (pp. 243-246).IEEE, 2013.

[10] Soni, K., Gupta, S. K., Kumar, U., & Agrwal, S. L., “A new Gaborwavelet transform feature extraction technique for ear biometricrecognition”, In 6th IEEE International Conference on Power India(PIICON), (pp. 1-3). IEEE, 2014.

[11] Monro, D. M., Rakshit, S., & Zhang, D., “DCT-based irisrecognition”, in IEEE Transactions on Pattern Analysis andMachine Intelligence, , vol. 29(4), pp. 586-595. 2007.

[12] Jyoti poonia, Parvati Bhurani, Rohit Kumar, Shubh LakshmiAgrawal, "Performance Review of IRIS Recognition Systems",International Journal of Computer Systems (IJCS), 2(12), pp: 564-566, December 2015.

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