1. Iris localization algorithm based on geometrical features of cow eyes Menglu Zhang Institute of Systems Engineering 2009.10.17

2.  Introduction  Cow iris recognition system  Methodology  Experimental results  Conclusions and future research Contents 1

3. 1 Introduction  As one of the most stable and efficient biometric identification methods, iris recognition has received more and more attention from many scholars, and has been widely used in human.  With the global development of industry and trade, abrupt vicious affairs on meat food show rapid expansion and spread widely, and pose a threat to food safety and human health. In order to trace the flow of meat products in the food supply chain, the research on the individual identification of large animals based on iris recognition has very important theoretical value and practical significance. 2

4.  Although iris recognition has been widely used in humans, the research and application in large animals are still in the experimental stage, and less relevant literatures exist in this academic field.  Masahiko Suzaki and Osamu Yamakita (2001) proposed a location algorithm and feature extraction algorithm according to the special structure of horse iris.  Fang Chao, Zhao Lindu (2008) discussed technical difficulties applying human iris recognition technology into the individual identification of large animals, and built a traceability system of meat products based on iris recognition. 3

5. 2 Cow iris recognition system  Differences between cow and human iris 4 Eyelid Iris Sclera Eyelashes Pupil Eyelashes Iris Sclera Pupil Figure 1. Differences between cow and human iris

6.  Based on the technology of Human Iris Recognition, the cow iris recognition systems are shown in Figure 2. Figure 2. Cow Iris Recognition System 5 Image acquisition Threshold segmentation edge detectioniris location image segmentation normalization iris coding

7. 3 Methodology On the basis of studying and analyzing geometrical features of cow eyes and the existing iris localization algorithms of human eyes, this paper proposes a cow iris localization algorithm.  Rough positioning ◎ threshold transform and Sobel edge detection ◎ the second B-spline curve fitting method  Fine positioning ◎ fitting concentric and non-concentric circles 6

8. 3.1 Threshold transform and Sobel edge detection  Threshold transform Cow iris image acquisition is usually carried out in the outdoors, therefore the facula is inevitable in the process of iris image acquisition. The pupil region is almost covered by the spot, which forms strong gray contrast between the pupil and the iris, iris and sclera, and makes the gradient of the boundary line larger, then the boundary line is easy to find. The global threshold method can be used to separate the iris and other parts quickly and efficiently, therefore we select model method to determine the threshold value. 1

9.  Sobel edge detection Sobel edge detection operator is selected for edge detection in this paper, and binarization processing is carried out before edge detection, which greatly enhances the accuracy and speed. Sobel operator focuses on pixels closed to the center point of the template, and the impact of noises is relatively small for the smoothing effect. Sobel operator retains sufficient information of the iris border while weakening the impact of other borders as far as possible, and then provides more precise edge direction information. 1 Figure 3. Sobel edge detection operator map

10. 3.2 Inner boundary fitting with quadratic B-spline interpolation curve  Assuming that is control vertexes in the given space, then the expression of quadratic B-spline curve can be defined as follows:  For the border points detected by Sobel operator, dynamic parameters method is selected to construct the quadratic B-spline interpolation curve passing the border points orderly in this paper.  Given that different border points of iris region and unit tangential vectors, we select the method constructing quadratic B-spline interpolation curve to obtain the inner boundary points. 1 Figure 4. Inner boundary fitting with quadratic B-spline interpolation

11. 3.3 Fitting concentric and non-concentric circles (1)fitting concentric circles Step1: For the inner boundary, we define a rectangular window according to the priori knowledge and the general pupil region. Scan on the selected region progressively to identify the first and last white pixel of each line, select the midpoint of the border points coordinates of the left and right side of the pupil region as the center abscissa, then the abscissa of the pupil center is as follows: Check the maximum distance from the inner boundary points to the center as the radius of the inner boundary circle, and the calculating formula is as follows: 1

12. Step2: For the outer boundary, we make the circle containing the largest number of outer boundary points after Sobel edge detection as the outer boundary circle. Take as the center and take as the initial. Gradually increase the radius of the given spacing. For each radius, we statistics the number of the corresponding boundary points respectively. The circle passing the largest number of boundary points is the most in line with the boundary circle, and the corresponding radius is the radius of the outer boundary fitting circle. 1

13. (2)fitting non-concentric circles The difference between the concentric and non-concentric circles is mainly in the center positioning, therefore this paper makes the following improvements based on the first algorithm: Step 1: For the inner boundary, the mean of horizontal (vertical) coordinates of all the points within the inner boundary is determined as the center of the inner border, and then the center coordinate is as follows: 1

14. Step 2: The methods determining the radius of the inner and outer boundary are remain unchanged, therefore we can obtain all the parameters and locate the iris boundary. (a) Fitting the concentric circle (b) Fitting the non-concentric circle Figure 5. Fitting concentric and non-concentric positioning

15. 4 Experimental results  Comparatively speaking, the latter algorithm of fitting non-concentric circles is more applicable to determine the inner and outer boundary parameters, and even more applicable to the cow iris image preprocessing.  Compared with the former, the latter can effectively fit the inner and outer boundary of the cow iris, exclude the spot region within the pupil and contain less sclera region. 7

16. 5 Conclusions and future research  On the basis of studying and analyzing lots of cow geometrical features and the existing iris localization algorithms of human eyes, this paper proposes a two-step localization algorithm of cow eyes. Though the human iris technology has obtained widely and deeply research and application, there are still many methodological and technical difficulties in the process of studying cow iris localization algorithm.  Applying the iris localization technology of human eyes into large animals still exists many problems. Therefore, how to realize the iris localization, normalization, feature extraction and matching on large animals more accurately, need to be further in-depth study. 8

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