ISSN(Online):2533 of Electronics & Communication Engineering, SRKR Engineering

ISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 22 IRIS Signa l Processing and Classification Using Compound Gabor Filter Prof. P.V.

Rama Raju and Mr. CH. Ganesh Dept. of Electronics & Communication Engineering, SRKR Engineering College , Bhimavaram – 534 204, A.

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P. (Affiliated to Andhra University, A.P.

, India) [email protected] , cell: 9010144688 ,[email protected] ,cell: 9491761366 Abstract – A resourceful iris processing method is suggested . In the sugge sted method the iris segmentation, normalization, feature extractions are based on the surveillance that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By noticing the edge between the pupil and the iris and the edge between the iris and the sclera, the iris area can be parted from pupil and sclera. Then the normalization technique is applied to reduce the dimensional inconsistencies later encoding technique is applied to extract the unique feature of iris ima ge, and the resulting one – dimensional (1 -D) signals are quantized using phase quantiz ation . The phase quantization pro cess produces a number of bits of information at various resolution levels over pixels on the normalized iris.

The Hamming distance is em ployed for classification , if the hamming distance between two templates is less than the set threshold va lue then two templates are declared to match or else they are not matched. A case identification program does the unique signal analy sis and completes the pattern classification. This program gives out its output in graphical form which indicates individual identification and gives a statement which makes an individual case identification . Keywords – Segmentation, Normalization, Feature extraction, Phase quantization . 1 INTRODUCTION A dictionary defines image as a “reproduction or representation of the form of a person or thing .

Digital image can be defined to be a numerical representati on of an object to be sampled in an equally spaced rectangular gri d pattern, and quantized in equal intervals of gray -level as function of two dimensions . The word is c arrying out for the simpler access controls to personal authentication systems and it looks like biometrics may be the answer. Hence human body can be use d to uniquely identify. Furthermore, biometrics is best defined as measurable physiological and/or behavio ral characteristics that can be utilized to verify the identity of an individual. They include the following: ? Iris scanning ? Facial recognition ? Fing erprint verification ? Hand geometry ? Retinal scanning ? Signature verification ? Voice verification 1.1 Iris Introduction The iris (irises ) is a thin, colored ring , circular structure in the eye, responsible for controlling the diameter and size of the pupi ls. The iris is called the “Living password” because of its unique, random features.

It’s always with human being and can’t be stolen or faked. As such it makes an excellent biometrics identifier and classifier. Figure 1 : Horizontal and v ertical view of Iris . 1.

2 Eye Pupil The pupil is a hole located in the center of the iris of the eye that allows light to enter the retina . It appears black because most of the light entering the pupil is absorbed by the tissues inside the eye . In response to the amount of light entering the eye, muscles attached to the iris expand or contract the aperture at the center of the iris, known as the pupil. The larger the pupil, the more light can enter.ISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 23 1.3 Iris Rec ognition methodology Figure 2: Flow diagram of the iris recognition steps .

To e xamine the recognition system, eye image will be used as input . Iris recognition relies on the unique patterns of the human iris to identify or verify the identity of an indiv idual . Eye localization and iris segmentation are sounds the same, These processes are done by using Canny edge detection technique, circular Hough transform (CHT) a standard computer vision algorithm, is commonly employed to deduce the radius and center coordinate s of the pupil and iris regions .The iris and pupil boundaries can be approximated as concen tric circles and t he outer boundaries of iris are detected with the help of center of pupi l.

For a particular circle the change in intensity between normal pointing toward center and away from center is measured. The radius having highest change in intensity is considered as outer boundary. Once the iris region is successfully segmented from an eye image, the next stage is to transform the iris region so tha t it has fixed dimensions in order to allow comparisons. Iris normalization is done in order to make the image independent of the dimensions of the input image. After iris is normalized , the algorithm is used to encode the iris data. This process extracts features from the normalized iris images and encodes it to generate iris templates . The resultant graphs shown in section4. For obtaining test parameter, Hamming distance gives a measure of how many bits are t he same between two bit templates .

Using the Ha mming distance of two bit patterns, a decision can be made as to whether the two patterns were generated from different irises or from the same one. 2 CASE STUD Y AND RESULT ANALYSIS After processing and analyzing the iris signal, The template of the each database signal have been plotted in Matlab as shown in corresponding figure s. The results are plott ed in terms of scales (Hamming distance) on the y -axis and the number of images in the database (sample images ) on x -axis. The matched result at that point is represented by a blue color bar with the threshold value . Red bar indicates that unmatched condition. In this exposition two cases are considere d under test. For every Image in the dataset the following values have been analyzed: 1. Iris radius and its center coordinates .

2. Filter parameters . 3.

Templates of irises . 4. Assignment in Databank. 5. Iris signal classification and identification. 3 INT ER CLASS SAMPLE DATABASE CLASSIFICATION Case1: Test signal – S1036 L0 7.

jpg (Left Eye) This signal has been taken from th e CASIA iris image database collected by Institute of Automation, Chinese Academy of Sciences . Figure 3 : Test signal left eye template. After processing and analyzing the iris signal, the template of the database signal has been plotted as shown in ab ove figure. In figure 3 the image is being tested S1036L07 .jpg, name reveals that seventh left eye sample image of a 36th perso n in the database.

ISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 24 Figure 4: Matched eye template from the database . Figure 4 show that the resultant matched template from the data base .

The Hamming distance distribution and matching condition between the templates are shown below. The result is plott ed in terms of scales (Hamming distance) on the y -axis and the number of images in the database (sample images ) on x -axis. Fig ure 5 : Tested image template matched with first template in the database In figure5 , the blue bar at position25 indicates that matching condition below the threshold value. Since database contains the different person’s data, tested image is matched with t he best compatible image.

The image is being used for test is matched with processed left eye images in the database corresponds to the same person, t he Hamming distance between tested template and matched template is taken as the optimum threshold value f or the same category. In the above example the optimum threshold value is set programmatically about 0.4, it will be differed for each individual test. Case2: Test signal – S1094R03 .

jpg (Right Eye) In this case the signal has been taken unlike t he above case, the right as tested eye. Figure 6: Test right sample template. After processing and analyzing the iris signal, the template of the database signal has been plotted as shown in above figure. In figure 6 the image is being tested S1094R03 .jpg, name reveals that third right eye sample image of a 94th person in the database. Figure 7 show that the resultant Hamming distance distribution and not -match condition between the templates are shown below, all the bar lines exceeds the threshold value line 0.4 , hence there is no match at any case, t he result is plotted in terms of scales (Hamming distance) on the y -axis and the number of images in the database (sample images) on x -axis.

Figure 7 : Tested image template unmatched with the any of template s in th e database The image is being used for test is a right eye not – matched with any of processed right eye images in the database corresponds to the same person, which ca n be observed at bars on x -axis . In the above example the optimum threshold value is set p rogrammatically aboutISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 25 0.4 which is shown as horizontal black line, it will be differed for each individual test. Hence test of above signal is done successfully and t he corresponding observations will be discussed in section 4 .

4 EXPERIMENTAL RESULT ANALYS IS 4.1 Filter parameters , Decidability, Template size, Number of shifts, Number of filters The best filter parameters were found through experimentation with the CASIA data set to compare perform ance. The decidability value determine s the optimum parameter s, those are wavelength, template size, number of shifts, number filters. Where d’ is decidability, mean of the intra -class distribution ?s and the mean of the inter -class distribution ?d and also the standard deviation of the intra -class and inter -class distributions ? s2, and ? 2d respectively. The results of experiment, various filter parameters to encode iris templates are observed bel ow table1 .

Table1: Different filter parameters with one filter with bandwidth 0.5 N 1 15 0.2 642 0.

039 0.4734 0.016 6.

067 1 16 0.2396 0.039 0.4725 0.012 6.

124 1 17 0.3775 0.039 0.4715 0.012 6.173 1 18 0.

2575 0.039 0.4705 0.013 6.

216 1 19 0.2601 0.039 0.4694 0.014 6.174 1 20 0.2810 0.

039 0.4683 0.015 6.130 1 21 0.2950 0.040 0.

4671 0.016 6.066 Figure 8: Decidability Vs center wavelength using filter bandwidth 0.5 One factor, which will significantly influence the recognition rate, is iris template size. For the CASIA data set, the optimum template size was found to be 20×240 pixels. Figure 8 show s that there exits an optimum center wavelength 18 for the data set, which produces maximum decidability d ‘ = 6.2160 . Furthermore, experiment results show that encoding templates with multiple filters does not produce better decidability values, ther efore the optimum number of filters is one and it produces a compact iris template.

The optimum ?/f value was found to be 0.50, Higher the decidability, greater the separation of same class and inter -class distributions, which allows for more accurate reco gnition. Figure 9 : Inter -class comparison with eight shifts As the number of shifts increases, the mean of the inter -class distribution will converge to an optimum value below the statistical value 0.5.

It can be seen in figure 9 . ‘ 22 () 2 sdsd d ???? ? ? ? min? s? s? d? d? ‘dISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 26 Figure 10 : Mean of in ter-class HD distribution Vs Number of shifts of two images of same class. With reference to Figure 10 , the CASIA data set requires 8 shifts to reach its minimum optimum threshold. However, it is noted that 4 -to-8 shifts are enough to be able to account for most of the rotational inconsistencies in the CASIA database.

5 OBSERVATIONS FROM THE PLOTS 1. The template generated for t he initial scales is multiplication of number of filters used to the signal processing, order of pixel array . 2. The 3D plot for 20 -by-24 0, color shift indicates shift of bits from 0 to 1 or 1 to 0 .

3. The decreasing scale number to 10 -by-1000 shown, easy to compare the given signal for each processing value. 4. The bar graph is generated with matching metric looks better and suitable for finding optimum signal classification . 5.

Blue bar indicates the matching condition, red bar indicates unmatched condition, and the circle line is the optimum threshold value on y -axis . 6. Inste ad of numerical observations comparison of signal analysis using the Hammin g distance bar graph yields better and accurate Identification . 6 CONCLUSION This exposition has presented an Inter -class iris recognition sy stem, which was tested using set of images as databases of greyscale eye images from different . Firstly, an effic ient iris processing method is processed. In the processed method the iris segmentation, normalization, feature extractions are being done. Then the encoding technique is applied to extract the unique feature of iris image, and the resulting one -dimensiona l (1 -D) signals are phase quantized using different dissimilarity functions.

The phase quantization process produces a bitwise template containing a number of bits of information at various resolution levels. The Hamming distance is employed for classifica tion of iris templates . The Matlab program gives out its output in graphical form which indicates individual identification and gives a statement which makes an individual case identification. 7 FUTURE SCOPE The system presented in this publi cation was able to perform Inter -class classification.

However there are still a number of issues which need to be addressed. First of all, Inter -class classification system to be implemented accurately. The automatic segmentation was not perfect, since it could not successfully segment the iris regions for all of the eye images in the database. Another extension to the system would be to interface it to an iris acquisition camera. REFERENCES 1 http://www.

csse.uwa.edu.au/~masekl01/ 2 Recognition of Human Iris Patterns fo r Biometric Identification , Libor Masek The University of Western Australia, 2003 3 http://en.wikipedia.org/wiki/Iris_(anatomy) 4 Canny, John, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.

PA MI-8, No. 6, 1986, pp. 679 – 698. 5 “A Human Identification Technique Using Images of the Iris and Wavelet Transform” W. W. Boles and B. Boashash 6 “Iris Recognition: An Emerging Biometric Technology” RICHARD P. WILDES, MEMBER, IEEE 7 Optimized Daugman’s Algorit hm for Iris Localization Dr.

Mohamed A. Hebaishy National Authority for Remote Sensing and Space Science Gozif Titp St., Elnozha Elgididah. Egypt (11769), Cairo. 8 “Recognition of Human Iris patterns for biometric identification”, Libor Masek, Dr.

Peter Koves i, The University of western Australis. 9 Exploit modeling for categorization of electrocardiogram signals through wavelet transform. prof.P.V.Ramaraju, V.Malleswara Rao, Mr CH.

Ganesh. 6 th international multi conference on intelligent system, IISN -2012, Mar ch 16-18,NIT Klawad,Haryana,India. 10 ECG signal processing and classification via heart rate fortitude in the vicinity of continuous wavelet transforms.prof.P.V.Rama raju, V.Malleswara ra o and Mr CH.

Ganesh, IEEE conference, International journal ACNCN -2012,A U college of engineering,visakhapatna.ISSN(Online):2533 -8945 VOLUME 4 ISSUE 6 PAPER AVAILABLE ON WWW.IJECEC.COM -VOLUME4 -ISSUE6 -NOV -2018 27 Prof. P.V.

Rama Raju received his Masters Degree in Microwave and Optical Engineering at the M.K. University, Tamil Nadu, India. He is a Professor at the Department of Electronics and Communication Engineering S.R.K .R.

Engineering College, AP, India. His research interests include bio medical -signal Processing, signal processing, VLSI design and microwave anechoic chambers design. He is author of several research studies published in national and international journals and conference proceedings.

Mr CH. Ganesh pursuing his Masters Degree in Communication Systems at S.R.K.R Engineering college , Bhimavaram, WG, Andhrapradesh , India. He is a studen t at the Department of Electronics and Communication Engineering S.

R.K.R. Engineering College, AP, India.