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Design and Development of New

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ABSTRACT

Biometric identifiers are the trait, measurable features used to tag and describe persons. Physical characteristics are related to the shape of the body. Some common examples of biometric recognition are, face recognition, fingerprint, DNA, palm print, iris, and retina. We propose a new algorithm for the detection and measurement of iris statistical features and finding the bifurcation points of retinal blood vessels for person identification, by using digital image processing techniques. Iris algorithm is tested on CASIA database and local database collected from KVKR (Department of CS and IT, Dr. B.A.M.U, Aurangabad) research lab, total 100 iris image database. For localization and extraction of inner iris we have use digital image processing techniques. After extraction of inner iris, we have calculated the statistical features like area, diameter, length, thickness, and mean. For performance analysis, receiver operating characteristic curve is used. The proposed algorithm achieves sensitivity of 94.92 % and specificity of 100%. After extraction of iris features, retinal blood vessels bifurcations points are extracted. Retinal image database is collected by Dr. Manoj Saswade (Opthalmologist, Saswade Netra Rugnalaya, Aurangabad (MH)), total 500 retinal image database. After collection of database, apply digital image processing techniques, such as image enhancement and otsu’s method. For result analysis, receiver operating characteristic (ROC) curve is use this algorithm achieves a true positive rate of 98%, false positive rate of 20%, and accuracy score of 0.9702.

General Terms

Person identification based on statistical techniques of retina and iris.

INTRODUCTION

Iris recognition has become an important permitting technology in our society. While an iris pattern is naturally a supreme identifier, the development of a high-performance iris recognition algorithm and transferring it from research lab to practical applications is still a challenging task. Iris is a physical biometric feature. It contains distinctive texture and is complex ample to be used as a biometric signature. Associated with other biometric features such as fingerprint, face, iris patterns are more stable and consistent. It is inimitable to people and stable with age. Also, iris recognition systems can be non-invasive. For localization of inner iris we have collected the 40 Iris images from CASIA image dataset [1]. And KVKR iris database is having 1000 iris images.This database is collected in department of computer science and information technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad.

Today’s e-security are in severe need of finding accurate, secure and cost-effective replacements to passwords and personal identification numbers as financial damages increase intensely year

over year from computer-based scam such as computer hacking and identity theft [2]. Biometric solutions report thesefundamental problems, because aperson’s biometric data is distinctive and cannot be moved. Biometrics which mentions to identifying an individual by his or her physiological or behavioral appearances has ability to distinguish between attributed user and an imposter. An advantage of using biometric authentication is that it cannot be lost or elapsed, as the person has to be physically present during at the point of identification process [3]. Biometrics is characteristically more reliable and accomplished than traditional information based and token based techniques. The commonly used biometric features include fingerprint, speech, iris, face, voice, hand geometry, retinal identification, and body smell identification [4].

METHODOLOGY

The proposed algorithm is design for localization of inner iris, shown in figure 2. In this algorithm firstly, preprocessing is done by renovating the image into gray. Afterwards apply histogram equalization for image enhancement. After image enhancement, image complement operation is done for highlighting the iris. Subsequently image adjustment is done by using contrast stretching method. After applying the contrast stretching function, some noise is get added, to remove that noise median filter is used. After removing the salt and pepper noise threshold operation is done for extraction of inner iris.In the figure 3, high resolution fundus image is taken then perform preprocessing operation on fundus image. Then perform image processing operation for enhancement of blood vessels. After enhancement of blood vessels perform threshold function for extraction of retinal blood vessels. Then perform morphologicalskeletonozation for calculating the centerline of blood vessels.Then perform minutia technique for labeling the bifurcation points. For performing this techniquesdatabase is taken from Dr. Manoj Saswade and Dr. Neha Deshpande this database have the 300 hundred high resolution fundus images, we have calculate bifurcation points for all 300 hundred images and store in one dataset and when new image came then it will match its bifurcation points with this dataset and it will give the result as match or not match.

Following are the mathematical formulations is use for extraction and localizing of inner iris.

Histogram equalization function for enhancing the gray image:

h(v)= round (cdf(v)−cdfmin(M×N)− cdfmin ×(L−1)) (1)

Here cdfmin is the minimum value of the cumulative distribution function, M × N gives the image’s number of pixelsand L is the number of grey levels.2D median filter is use for removing the salt and pepper noise.

y [m,n]= median{x[i,j],(i,j)∈ ω} (2)

Here ω Represents a neighborhood centered around location (m, n) in the image.

Threshold function for extracting the retinal blood vessels. ????=12(????1+????2) (3)

Here m1 & m2 are the Intensity Values.

RESULT

By using digital image processing techniques we have extract the inner iris following figure 2 shows the output of inner iris localization. After extraction of inner iris we have calculated the statistical features like area, diameter, length, thickness and mean.

Original Image

Extracted Inner Iris

Localization of Inner Iris

A. Blood Vessels extraction :

Use the complement function for enhancing the blood vessels of the retina. Following formula is the mathematical representation of Complement function.

Ac={ω |ω∉A } (4)

Here Ac is a complement, ω is the element of A, ∉ stands for not an element of A and A is set.

Then use Histogram equalization function for enhancing the complementary image to adjustment of contrast for better quality of an image. Histogram equalization is very important method for enhancement, the following mathematical equation elaborate the histogram equalization

h(v)= round (cdf(v)−cdfmin(M×N)− cdfmin ×(L−1)) (5)

Here cdfmin is the minimum value of the cumulative distribution function, M × N gives the image’s number of pixels and L is the number of grey levels.

After enhancement, use the Morphological structuring element for enhancing the blood vessels of the retina. The following mathematical formula shows the dilation and erosion function.

Idilated (i,j)= maxf(n,m)=trueI(i+n,j+m) (6)

Ieroded (i,j)= minf(n,m)=trueI(i+n,j+m) (7)

Perform erosion and dilation for joining the corrupted blood vessels. After performing these operations, the result is shown in figure 5.

Color Fundus Images

Blood Vessels extracted images

CONCLUSION

For localization and extraction of inner iris we have use digital image processing techniques depicted in figure 2. For analysis of this techniques we have use online CASIA database and local database collected from KVKR research lab (Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad).After extraction of inner iris, we have calculated the statistical features like area, diameter, length, thickness, and mean. For performance analysis, receiver operating characteristic curve is used. The proposed algorithm achieves sensitivity of 94.92 % and specificity of 100%. And for retinal blood vessels bifurcations points, design new algorithm. For result analysis, receiver operating characteristic (ROC) curve is use this algorithm achieves a true positive rate of 98%, false positive rate of 20%, and accuracy score of 0.9702.

5. ACKNOWLEDGMENTS

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