Implementation of Reliable Open Source IRIS Recognition System

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1 Implementation of Reliable Open Source IRIS Recognition System Dhananjay Ikhar 1, Vishwas Deshpande & Sachin Untawale 3 1&3 Dept. of Mechanical Engineering, Datta Meghe Institute of Engineering, Technology & Research, Wardha Ramdeobaba Collge of Engineering & Management, Nagpur dhananjayikhar@yahoo.com, deshpandevs@rknec.edu, untawale@gmail.co Abstract RELIABLE automatic recognition of persons has long been an attractive goal. As in all pattern recognition problems, the key issue is the relation between inter-class and intra-class variability: objects can be reliably classified only if the variability among different instances of a given class is less than the variability between different classes.the objective of this paper is to implement an open-source iris recognition system in order to verify the claimed performance of the technology. The development tool used will be MATLAB, and emphasis will be only on the software for performing recognition and not hardware for capturing an eye image. A reliable application development approach will be employed in order to produce results quickly. MATLAB provides an ecellent environment, with its image processing toolbo. To test the system, a database of 756 grayscale eye images courtesy of Chinese Academy of Sciences-Institute of Automation (CASIA) is used. The system is to be composed of a number of sub-systems, which correspond to each stage of iris recognition. These stages are- image acquisition, segmentation, normalization and feature encoding. The input to the system will be an eye image, and the output will be an iris template, which will provide a mathematical representation of the iris region. Which conclude the objectives to design recognition system arestudy of different biometrics and their features? Study of different recognition systems and their steps, selection of simple and efficient recognition algorithm for implementation, selection of fast and efficient tool for processing, apply the implemented algorithm to different database and find out performance factors. Inde Terms Biometrics, iris Recognition, Biometrics, Iris image quality, fingerprint, iris Recognition, Normalisation I. INTRODUCTION THE ANTICIPATED large-scale applications of biometric technologies such as iris recognition are driving innovations at all levels, ranging from sensors to user interfaces, to algorithms and decision theory. At the same time as these good innovations, possibly even outpacing them, the demands on the technology are getting greater. The iris is an eternally visible and well protected organ whose unique epigenetic pattern remains stable throughout adult life. These characteristics make it very attractive for use as a biometric for identifying individuals. Image processing techniques can be employed to etract the unique iris pattern from a digitized image of eye, and encode it in to biometric template, which can be stored in a database. This biometric template contains an objective mathematical representation of unique information stored in iris, and allows comparisons to be made between templates. When a subject wishes to be identified by iris recognition system, their eye is first photographed, and then a template created for their iris region. This template is then compared with other templates stored in database until either a matching template is found and the subject is identified, or no match is found and the subject remains undefined. So basis of every biometric trait is to get the input signal/image apply some algorithm and etract the prominent feature for person identification/verification. In the identification case, the system is trained with the patterns of several persons. For each person, a template is calculated in training stage. A pattern that is going to be identified is matched against every known template. In the verification case, a person's identity is claimed a priori. The pattern that is verified only is compared with the person's individual template. Most biometric systems allow two modes of operation, an enrolment mode for adding templates to database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. Following figure differentiates these enrolment and identification process clearly. 43

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) II.

But it is depend on surrounding intensity, lightning used, and camera resolution and distance between camera and user s eye.

It is taken from The Centre of Biometric and Security Research, CASIA Iris Image Database. A.

2 International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) II. IMPLEMENTATION Implementation of good image acquisition system is very difficult because it is epected that from system to obtain noise free image. But it is depend on surrounding intensity, lightning used, and camera resolution and distance between camera and user s eye. LED is most commonly used light source than IR LED because its light affects the human eye system. In this thesis CASIA DATABASE eye images are used which includes both noise free and noisy images. It is taken from The Centre of Biometric and Security Research, CASIA Iris Image Database. A. Image Segmentation In pupil detection, the iris image is converted into grayscale to remove the effect of illumination. As pupil is the largest black area in the intensity image, its edges can be detected easily from the binarized image by using suitable threshold on the intensity image. Thus the first step to find or separate out the pupil apply histogram of input image from which we get threshold value for pupil, then apply edge detection, once edge of pupil find, then center coordinates and radius can be easily find out by following algorithm having steps-(a) Find the largest and smallest values for both and y ais. (B)Add the two -ais value and divide them by two will gives - center point. (C)Similarly add two y-ais values, divide it by two, gives y- center point. (D) Radius is calculated by subtracting minimum value from maimum and divides it by two gives the radius of pupil circle. (a) (b) (c) Fig 1 (a) Canny edge image (b) Only pupil, (c) Pupil ring Eyelash and eyelid always affects the performance of system. The eyelashes are treated as belonging to two types, separable eyelashes, which are isolated in the image, and multiple eyelashes, which are bunched together and overlap in the eye image. In this thesis iris circle diameter is assumed as two times pupil diameter and the noise, eyelash and eyelid, are avoided by considering lower portion of iris circle. Hence after segmentation a complete iris part is separate out. Shown below (a) (b) (c) Fig. (a) Selected Iris Circle (b) Sharpened Iris (c) Iris Part with Eyelashes B. Normalization The Daugman s rubber sheet model remaps each point within the iris region to a pair of polar coordinates ( ) where r is on interval [0, 1] and is angle [0, ] Fig.3. Daugman s rubber sheet model The remapping of the iris region from (, y) Cartesian coordinates to the normalized non-concentric polar representation is modeled as I( ( ), y( )) I( ) with ( ) (1 r) y( ) (1 r) y p ( ) r ( ) p ( ) r y l l ( )..(1) Where I (, y) is the iris region image, (, y) are the original Cartesian coordinates, ( r, ) are the corresponding normalized polar coordinates, and p, y p and l, yl are the coordinates of the pupil and iris boundaries along the direction. The rubber sheet model takes into account pupil dilation and size inconsistencies in order to produce a normalized representation with constant dimensions. In this way the iris region is modeled as a fleible rubber sheet anchored at the iris boundary with the pupil centre as the reference point.even though the homogeneous rubber sheet model accounts for pupil dilation, imaging distance and non-concentric pupil displacement, it does not compensate for rotational inconsistencies. In the Daugman system, rotation is accounted for during matching by shifting the iris templates in the 44

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) direction until two iris templates are aligned.

The centre of pupil is considered as the reference point, and radial vector pass through the iris region, as shown in fig.

3 International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) direction until two iris templates are aligned. For normalization of iris regions a technique based on Daugman s rubber sheet model is employed. The centre of pupil is considered as the reference point, and radial vector pass through the iris region, as shown in fig. Cartesian coordinates of data points from the radial and angular position in the normalized pattern. From the doughnut iris region, normalization produces D array with horizontal dimensions of angular resolution and vertical dimensions of radial resolution. Another D array is created for making reflections, eyelashes, and eyelids detected in the segmentation stage. In order to prevent non-iris region data from corrupting the normalized representation, data points which occur along the pupil border or the iris border are discarded. As in Daugman s rubber sheet model, removing rotational inconsistencies is performed at the matching stage and will be discussed in the net chapter. Fig.4. Normalization A number of data points are selected along each radial line and this is defined as the radial resolution. The number of radial lines going around the iris region is defined as the radial resolution. Since the pupil can be non-concentric to the iris, a remapping formula is needed to rescale points depending on the angle around the circle. This is given by r' with o o y o cos arctan o r y l.. () Where displacement of the centre of the pupil relative to the centre of the iris is given by o, oy and r' is the distance between the edge of the pupil and edge of the iris at an angle, around the region, and rl is the radius of the iris. The remapping formula first gives the radius of the iris region doughnut as a function of the angle.a constant number of points are chosen along each radial line, so that a constant number of radial data points are taken, irrespective of how narrow and wide the radius is at a particular angle. The normalized pattern was created by backtracking to find the C. Feature Encoding Fig.5. Normalized iris part 1. Radial and Circular Feature Encoding This approach is based on edge detection.edges are detected in input image using canny edge detector. After edge detection image is changed to binary format in which white piels are present on edges and black piels elsewhere. The number of white piels in radial direction and on circle of different radius gives important information about iris patterns. Normalized polar iris image will contain only white and black piels as it is obtained from above edge detected input image. Features from normalized images are etracted in two ways (a) radial way (b) circular way. (a) Radial features Fig.6. Feature etraction in radial direction In iris image value of radial feature at particular angle will be number of white piels along the radial direction. If 45

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) S r, 1 iris _ polar _ image[ r][ ] WHITE 0 iris _ polar _ image[ r][ ] BLACK

For matching compare the two images by using Subtraction of white piels available in database image from number of white piels available in query image.

Feature etraction in circular direction In iris image value of circular feature at particular radius will be considered as sum of white piels along the circle of that radius.

. (4) Iris code will be considered as sequence of radial and circular features.

4 International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) S r, 1 iris _ polar _ image[ r][ ] WHITE 0 iris _ polar _ image[ r][ ] BLACK Feature at angle will be N F S r, r 1 (3) 3. Total numbers of white piels are stored. 4. Similar steps from 1 to 3 are followed for both the query and data base image. 5. For matching compare the two images by using Subtraction of white piels available in database image from number of white piels available in query image. (b) Circular features Fig.7.Feature etraction in circular direction In iris image value of circular feature at particular radius will be considered as sum of white piels along the circle of that radius. Keeping the meaning of S same.the feature of particular radius r will be given as following. F r S 0.. (4) Iris code will be considered as sequence of radial and circular features. In this method number of white piel on radial and circular direction is measured which then indicates code for that particular eye image. It is obtained by following steps. 1. Image in polar form is converted into binary form. Fig.9. Circular Feature Fig.10. Radial Features Fig.8. Normalized image converted into binary. Number of white piels in radial and circular direction is measured. White piels [counts] = 1059 Fig11.(a)Query Image (b)retrieved Image Black piels [] = 7041 Fig.1. Normalized image 46

5 Absolute difference International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) D. Matching The most commonly used metric for matching the two bit strings generated by query image and template stored in database is the Hamming Distance. It is a simple XOR operation where result equal to zero when both said a string has same bit string. Although, in theory, two iris templates generated from the same iris will have a hamming distance of 0.0, in practice this will not occur. Because normalization is not perfect, and also there will be some noise that goes undetected so some variation will be present when comparing two intra-class iris templates. III.PERFORMANCE EVALUATION The performance of the iris recognition as whole is eamined. Tests were carried out to find the best separation. So that the false match and false accept rate is minimized, and to conform that iris recognition can perform accurately as a biometric for recognition of individuals. As well as confirming that the system provides accurate recognition, eperiments were also conducted in order to confirm the uniqueness of human iris patterns by reducing the number of degrees of freedom present in iris template representation. The points which decide the performance of systems are- 1. False Acceptance Rate [FAR]. False Rejection Rate [FRR] 3. Equal Error Rate [EER] 4. Accuracy5. Decidability6. FAR and FRR according to hamming distance. Figure and Table bmp 10.bmp Database images vs Absolute difference Fig.13. Database images vs. HD Database images 11.bmp 1.bmp 13.bmp 14.bmp 15.bmp 16.bmp.bmp 3.bmp Database images 1.bmp bmp bmp bmp 80 Absolute difference 4.bmp 5.bmp 6.bmp 7.bmp 8.bmp 9.bmp Series1 13.bmp bmp bmp bmp 806.bmp 80 3.bmp 0 4.bmp 85 5.bmp bmp bmp 86 8.bmp 79 9.bmp 89 Performance Evaluation for Circular and radial 1. False Accepting Rate:-The fraction of the number of accepted client patterns divided by the total number of client patterns is called False Rejection Rate (FAR). Number of times different person matched 100 FAR Number of comparisionbetween different persons.false Rejecting Rate:-The fraction of the number of rejected client patterns divided by the total number of client patterns is called False Rejection Rate (FRR). Number of times same person rejected 100 FRR Number of comparision between same person 3. Decidability (5) (6) The key objective of a recognition system is to be able to achieve a distinct separation of intra-class and inter-class hamming distances. The separation between inter class and intra-class hamming distance distributions can be measured by metric decidability and is given by the following formula. Higher the value of decidability better is the performance of system. Actually it decides the separation of FAR and FRR. Note that if the score distributions overlap, the FAR and FRR intersect at a certain point. The value of the FAR and the FRR at this point, which is of course the same for both of them, is called the Equal Error Rate (EER) and in above graphs it, is obtained for image 3.bmp where HD is same for FAR and FRR(0.3778). 47

6 HD International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) Decidability and EER Graph 1.bmp 1.bmp 1.bmp.bmp.bmp 3.bmp images Fig.16. Graph of Decidability IV. CONCLUSION AND FUTURE SCOPE Series1 Series An iris recognition methods proposed in this thesis employs iris feature etraction using a cumulative-sumbased change analysis and Radial and Circular method. In order to etract iris features, using a cumulative-sumbased change analysis, a normalized iris image is divided into basic cells. Iris codes for these cells are generated by proposed code generation algorithm which uses the cumulative sums of each cell. The method is relatively simple and efficient compared to other eisting methods. Eperimental results show that the approach of implemented method has good recognition performance and speed. In future, to make the system more robust and reliable need to eperiment on a larger iris database. The performance of second method is not encouraging because absolute difference between two templates is considered for matching purpose. Also this algorithm is based on result of edge detection and edge detection algorithms are not efficient for illumination in images. Some edges cannot be detected if image is taken in low illumination condition. V. REFERENCES [1] J.G.Daugman High confidence Visual Recognition of Persons by a Test of statistical Independence IEEE Trans. Pattern analysis and machine Intelligence, Vol.15, no.11,1993,pp [] W.W.Boles and B.Boashash, A Human Identification technique using images of Iris and Wavelet Transform, IEEE trans.signal processing, Vol.46, no.41998, pp [3] Li Ma and T.Tan, Personal identification based on iris teture analysis, IEEE Trans.Pattern analysis and Machine Intelligence, Vol.5, no.1, 003. [4] J. Matey, K. Hanna, R. Kolcyznski, D. LoIacono, S. Mangru,O. Naroditsky, M. Tinke T. Zappia, and W-Y. Zhao, Iris on the move:acquisition of images for iris recognition in less constrained Environments, Proc. IEEE, vol. 94, no. 11, pp , Nov [5] J. G. Daugman, How iris recognition works, IEEE Trans. Circuits Syst.Video Technol., vol. 14, no. 1, pp. 1 30, Jan

Enhanced Iris Recognition System an Integrated Approach to Person Identification

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