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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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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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