An Efficient Iris Recognition Using Correlation Method

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1 , pp An Efficient Iris Recognition Using Correlation Method S.S. Kulkarni 1, G.H. Pandey 2, A.S.Pethkar 3, V.K. Soni 4, &P.Rathod 5 Department of Electronics and Telecommunication Engineering, Thakur College of Engineering & Technology, Kandivali(E), Mumbai 1 kulkarni.sujata@yahoo.com** 2 i.gauravpandey@yahoo.com 3 ameyapethkar@yahoo.com 4 vishal_soni8488@yahoo.com 5 prashantrathod024@gmail.com Abstract: In this paper we propose the algorithm, which is different from Daugamn s iris recognition algorithm. In the proposed algorithm the segmentation and matching method is quite different from Daugman s method. We segment the image by considering only boundary of pupil. The localization of pupil can be done using thresholding and plotting virtual circle of proper radius can segment the region of interest closer to pupil boundary. Normalization is done using Daughman s rubber sheet model. To remove the intensity variation due to varying lighting condition the histogram equalization is used. Matching has done by the correlation method. Since the two dimensional correlation gives the extent of similar distribution of pixel values, it can be used to identify that iris belongs to same person or not. Recognition algorithm is implemented in MATLAB software. An experimental result shows false acceptance rate (FAR) and false rejection rate (FRR) of system. According to the result we conclude that the proposed algorithm accuracy is good and it is efficient and adaptive to environment, it works well even for noisy environment, variable illumination. Key Words: Iris, Segmentation, Normalization, Correlation, Efficient Methodology: In this paper we are not using hardware for capturing an iris. The designed system has been tested on the CASIA (The Chinese Academy of Sciences Institute of Automation) database [3]. The dataset has 756 grayscale non-ideal eye images that come from 108 different users with 7 images per user. Recognition algorithm is implemented in MATLAB software. MATLAB software provides an excellent rapid application development with its image processing toolbox, and high level programming methodology [1].

An Efficient Iris Recognition Using Correlation Method I. Introduction to Iris Recognition 1.1.1 The Human Iris Fig (1.1) shows the frontal portion of iris.

The amount of light entering through the pupil is controlled by iris through the sphincter and the dilator muscles, which adjust the size of the pupil.

2 An Efficient Iris Recognition Using Correlation Method I. Introduction to Iris Recognition The Human Iris Fig (1.1) shows the frontal portion of iris. It is a thin circular diaphragm, present between the carmera and the lens of the human eye. The circular aperture present at centre is called as pupil; from its center iris portion is very close. The amount of light entering through the pupil is controlled by iris through the sphincter and the dilator muscles, which adjust the size of the pupil. The average diameter of the iris is 12 mm, and the pupil size can vary from 10% to 80% of the iris diameter [1]. Fig 2.1 Daugamn Iris recognition algorithm Fig.1.1 A front-on view of the iris. An iris recognition system has following sub systems: 1) Image acquisition 2) Image preprocessing 3) Feature Extraction and 4) Decision Making. [4] Fig.1.2 An Iris recognition system Fig(1.2) elaborate the above process. The first step is capture the eye image, captured image is then undergo for preprocessing to test the quality of images, if it is good enough then first locate the iris in the captured image. If quality is not good enough then enhancement is necessary. Localization of iris is very important; if iris cannot be localized correctly then system will fail for recognition. Once the iris is localized then next step is normalization i.e. normalized into rectangular images with predefined size. Then features get extracted from the normalized images. The last stage is matching of iris, i.e. compare the feature extracted image to a image stored in database. II. Daugamn Iris Recognition Fig (2.1) shows Daugaman iris recognition algorithm.[2] [4-5] In the above figure, first capture the eye image of size 480X640. The first stage is segmentation in which iris region is located. Actual iris is circular region present between the outer and inner circles i.e iris boundary and pupil boundary. In order to detect these boundary, circular Hough transform is used. The segmented iris region needs to be aligned to a fixed size using normalization. Normalization is performed using Daugman s rubber sheet model. Feature extraction has done by 2D Gabor wavelet filters. Extracted features represents in iris patterns described by iris code.to perform the recognition two iris codes are compared. Hamming distance is used to test the statistical independence between two iris codes. [2] III. Concept of Proposed Algorithm Fig (3.1) shows the steps of proposed algorithm. [6] Pictorial view of this algorithm is same as Daugaman algorithm but the implementation technique in segmentation and matching is different Segmentation Segmentation is the procedure in which the actual iris region separated in digital eye image. In order to implement Canny, it requires edge linking procedure using Hough transform. For each link between two points we need (n-1) n/2 no of calculation and it requires many time for linking one complete edge. Thus it reduces the speed of algorithm. In order to localize iris, complete iris is not required. So we consider the region of interest which is located near the boundary of pupil. The localization of pupil can be done using thresholding. As image is considered as a square matrix then the pixels of same value is assumed as high and rest of pixel is low. (by proper thresholding we get image shown in Fig(3.2)(B) and the region of interest closer to pupil boundary can be segmented by plotting virtual circle of proper radius. For virtual circle if the value of the pixels is repeated and if such pixels are more than 20 then we assume the high, else low then we get Fig (3.3). 32

SS Kulkarni, GH Pandey, ASPethkar, VK Soni, & PRathod A C D Fig.3.4A) Selected image) Horizontal D) Vertical Diameter Fig 3.1

People may be captured the images in different size, that affect in recognition result.

Normalization has done by using Daugamn rubber sheet model [1] as shown in Fig(.3.5).We unwrapped the iris ring to rectangular texture by 180 to 360 as shown in Fig 3.6(E), (3.6)(F Fig3.

4)(C). For vertical diameter use same technique. Once we get the both diameter it becomes easy to detect the center as shown in Fig (3.4) (D). As shown in Fig3.

For actual diameter we consider the average of the maximum range of the rows plus one then calculates the radius of the circle. Fig 3.3 Detection of pupil and sclera Boundary E Fig.3.5 Iris Normalization Fig 3.

3 SS Kulkarni, GH Pandey, ASPethkar, VK Soni, & PRathod A C D Fig.3.4A) Selected image) Horizontal D) Vertical Diameter Fig 3.1 Steps of Proposed Algorithm Normalization Pupil is very sensitive to illumination, so if illumination changes the pupil size of same eye varies. People may be captured the images in different size, that affect in recognition result. In order to become uniform size the circular pattern of iris in rectangular representation is called normalization. Normalization also reduces the distortion caused by pupil movement. Normalization has done by using Daugamn rubber sheet model [1] as shown in Fig(.3.5).We unwrapped the iris ring to rectangular texture by 180 to 360 as shown in Fig 3.6(E), (3.6)(F Fig3.2 A) Selected image B) Threshold image Using threshold image, detect horizontal diameter by making the addition of all rows, then detect those rows whose value is high, as shown in Fig(3.4)(C). For vertical diameter use same technique. Once we get the both diameter it becomes easy to detect the center as shown in Fig (3.4) (D). As shown in Fig3.2 (B) the pupil boundary is look like circular which is not exactly the circle so calculated center is not a perfect centre. For actual diameter we consider the average of the maximum range of the rows plus one then calculates the radius of the circle. Fig 3.3 Detection of pupil and sclera Boundary E Fig.3.5 Iris Normalization Fig 3.6 E) Unwrapped from 180 to 360 F) Unwrapped template without effect to eyelid Enhancement The normalized iris image has non uniform brightness because of the light variation. That affects the further procedure such as features extraction and matching. To make uniform distribution of intensity across the whole image, The mean of each 16x16 small block constitutes a coarse estimate of the background illumination [6]. This estimate is further expanded to the same size F 33

An Efficient Iris Recognition Using Correlation Method as the normalized image by bicubic interpolation.

Then we enhance the lighting corrected image by histogram equalization [6] Histogram equalization image is as shown in Fig 3.7. Where mx and my are the means of the corresponding series.

The correlation coefficient at lag k of a series x 0, x 1, x 2,...x N-1 is normally given as[7] G Where mx is the mean of the series.

To select image CLICK on ENTER Fig.3.7 G) Histogram Equalization H) &I) Intensity Variation by Histogram IV. Matching In iris recognition, the matching by correlating the two normalized irises. 1.4.

4 An Efficient Iris Recognition Using Correlation Method as the normalized image by bicubic interpolation. The estimated background illumination is subtracted from the normalized image to compensate a lighting condition. Then we enhance the lighting corrected image by histogram equalization [6] Histogram equalization image is as shown in Fig 3.7. Where mx and my are the means of the corresponding series. If the above is computed for all delays d=0,1,2,...n Auto Correlation When the correlation is calculated between a series and a lagged version of itself it is called autocorrelation. The correlation coefficient at lag k of a series x 0, x 1, x 2,...x N-1 is normally given as[7] G Where mx is the mean of the series. When the term i+k extends past the length of the series N two options are available. H I V. S/W Implementation Step1: When program starts the following screen appears. To select image CLICK on ENTER Fig.3.7 G) Histogram Equalization H) &I) Intensity Variation by Histogram IV. Matching In iris recognition, the matching by correlating the two normalized irises Correlation The correlation is one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables. When the images are compared with it then the correlation becomes autocorrelation Cross Correlation Cross correlation is a standard method of estimating the degree to which two series are correlated. Consider two series x (i) and y (i) where i=0,1,2...n- 1. The cross correlation r at delay d is defined as [7] Step2: Now CLICK on SELECT EYE IMAGE 34

5 SS Kulkarni, GH Pandey, ASPethkar, VK Soni, & PRathod Step3: The following dialog box appears. In that select the folder eye image of iris recognition Step7: Select the profile from the PROFILE folder Step4: Then select any image from the 1 to 20 image of different condition Step8: Select the folder mentioned below. Step5: To add the image into the database CLICK on TO CREATE DATABASE Step 6:The following screen appears saying SELECT PROFILE 35

6 An Efficient Iris Recognition Using Correlation Method Step9: Select the respective profile for the respective image Step10: Then the image with profile is stored in the database Step12: CLICK on START RECOGNITION Step13: If the image is found then the profile of the person is displayed as shown below. Step14: The entire Iris recognition step is shown below. To add the images with their profile in database repeat step 2 to 10 Step11: For iris recognition, select a image by repeating STEP 2 to STEP 4. then the following screen will appear. CLICK on FOR IRIS RECOGNITION VI. Experimental results Extensive experiments on a reasonably sized image database are performed to evaluate the effectiveness and accuracy of the proposed method. The experiments are completed in two modes: identification (one-to-many matching) and verification (one-to-one matching). 36

7 SS Kulkarni, GH Pandey, ASPethkar, VK Soni, & PRathod Two commonly used performance measures derived from the ROC curve and Equal Error Rate (EER).We add the different noise in the image and test the algorithm. For salt and pepper noise the result is good as shown below. Table 1. Without Salt and Pepper Noise PARAMETERS FMR FNMR Without illumination 0.72% 0.6% With illumination 0.68% 1.7% overall 0.714% 1.02% Table 2. With Salt and Pepper Noise PARAMETERS FMR FNMR Without illumination 0.75% 0.58% With illumination 0.70% 1.2% overall 0.74% 1.1% Table 3. Performance comparisons of some popular iris recognition algorithms with our for CASIA v1.0 iris database METHODOLOGY ACCURA CY RATE Daugman (Daugman, J. (1993)) 54.44% Wilds (Wilds, R. (1997)) 86.49% Masek (Masek, L. (2003)) 83.92% Liam and Chekima (Liam, L.W., & 64.64% Chekima, A., & Fan, L.C., & Dargham, J.A. (2002)) Our Method >90% Fig 3.8 Graphical presentation of popular iris recognition algorithm VII. Conclusion This paper has presented an iris recognition system, which was tested using CASIA databases of grayscale eye images in order to verify the claimed performance of iris recognition technology. According to data present in Table 3, it is clear that the proposed method is efficient as compare to other methods with accuracy of more than 90%. From Table1 and Table2 it shows that noise and illumination can not make effect on the performance. VIII. Future Scope The system presented in this paper is able to perform accurately, however there are still a number of issues which need to be addressed. First of all, the segmentation was not perfect, since it could not successfully segment the iris regions for all of the eye images in the databases. In order to become the optimize code apply this method to another database such a LEI database. An improvement could also be made in the speed of the system. The system is implemented in MATLAB, which is an interpreted language, speed benefits could be made by implementing computationally intensive parts in C or C++. Speed was not one of the objectives for developing this system, but this would have to be considered if using the system for real-time recognition REFERENCES [1] Libor Masek. Recognition of Human Iris Patterns for Biometric Identification Bachelor of Engineering degree of the School of Computer Science and Software Engineering, The University of Western Australia, 2003 [2] J. Daugman. How iris recognition works Proceedings of 2002 International Conference on Image Processing, Vol. 1, [3] Chinese Academy of Sciences Institute of Automation. Database of 756 Grayscale Eye Images. [4]J.Daugman, Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns, International Journal of Computer Vision, 2001 [5]J.Daugman, Biometric Personal Identification System Based On Iris Analysis, US Patent , [6] Li Ma, Tieniu Tan, Dexin Zhang, Yunhong Wang Local Intensity Variation Analysis for Iris Recognition National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. [7]Paul Bourke Cross Correlation,Autocorrelation -- 2D Pattern Identification August 1996 [8] Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki An Iris Recognition System Using PhaseBased Image Matching Graduate School of Information 37

8 An Efficient Iris Recognition Using Correlation Method Sciences, Tohoku University, Sendai , Japan [9] W. Boles and B. Boashash, A Human Identification Technique Using Images of the Iris and Wavelet Transform, IEEE Trans. on Signal Processing, Vol.46, No.4, pp , [10] J.Daugaman. Probing the Uniqueness and Randomness of IrisCodes :Results From 200 Billion Iris Pair Comparisons

Critique: Efficient Iris Recognition by Characterizing Key Local Variations

Critique: Efficient Iris Recognition by Characterizing Key Local Variations Authors: L. Ma, T. Tan, Y. Wang, D. Zhang Published: IEEE Transactions on Image Processing, Vol. 13, No. 6 Critique By: Christopher