improved authentication using iris recognition · canara engineering college mangalore njciet 2015...

ISBN: 97-8-93-81195-82-6 PROCEEDINGS OF NJCIET 2015

Canara Engineering College Mangalore NJCIET 2015 224

Improved Authentication using Iris Recognition

Mranila P1, Reshma K. J1

Dept. of Electronics and Communication St. Joseph Engineering College

Mangaluru, India {mranila04, reshmasushanth}@gmail.com

Abstract. Today authentication is very important to improve the security in various applications. Iris recognition, a biometric, provides one of the most secure methods of authentication .This paper proposes an iris recognition algorithm with the help of curvelet transform. It consists of five major steps i.e., Preprocessing, Segmentation, Normalization, Feature extraction and Matching. The inner pupil boundary and outer iris boundary is localized using Circular Hough Transformation, which is applied after canny edge detection. The localized iris image is segmented and transformed into rectangular area of fixed dimensions using Daugman‘s rubber sheet model. Iris features are obtained from curvelet transform coefficients that are obtained by applying curvelet transform on the normalized image. For identification, the iris code of a test image is matched against the whole data file of stored iris codes using

hamming distance method. The lowest Hamming distance value is the best match between two templates. The system is tested on Casia iris images using Matlab.

Keywords: Circular Hough transform, Curvelet transform, Hamming distance.

1 Introduction

The need for personal identification has increased a lot in recent times [2]. A biometric system provides automatic recognition of an individual based on some unique features possessed by the individual [3]. The iris recognition is comparatively better than other methods because of its uniqueness, non-invasiveness and stability of human iris [2]. It can be used instead of usernames, passwords and smart cards [5].

The Fig. 1 shows the structure of human eye. The part carrying the information required by the iris recognition is the iris, so other parts have to be eliminated. The human iris is an coloured region between the pupil and sclera[1].

The Daugman‘s method of iris recognition provides high accuracy and is employed in several commercial iris recognition systems. But the drawback of this method is its long length of the iris code by considering the large volume of iris data that has to be compared. Wildes [8 ] represented the iris texture with a Laplacian pyramid, which is constructed with four different resolution levels and determines whether the input image and the model image are from the same class using normalized correlation. It gives lower accuracy and has high computational complexity[1]. Since only the coefficients from approximation sub bands are used, the length of the code is reduced.

In this paper, iris image is pre-processed to a fixed size and edges of the iris image are detected using the canny edge detector. Segmentation of iris from an eye image is performed using Hough transform. The iris image is then normalized using Daugman‘s Rubber Sheet Model. The feature extraction process is performed by curvelet transform on the normalized iris image, followed by the matching process that is carried out using Hamming distance method.

Figure 1: Eye image.



2 IRIS RECOGNITION SYSTEM

The iris recognition system is mainly divided into two phases, namely, enrollment and identification phase. The Figure 2 shows the various stages of a typical iris recognition system. The first stage of iris recognition system is the iris preprocessing followed by the segmentation which is the second stage. The main objective of this stage is to localize the iris and pupil, to determine its centers and radii, and to segment the iris part. The third stage is the iris normalization to transform the circular iris region into rectangle shape, which is of fixed dimensions. To provide accurate recognition of individuals, the unique features present in an iris pattern has to be extracted which is done in stage four. And in stage five comparisons between the templates from the iris image that has to be recognized is compared with the templates in the data file.

2.1 Iris Image Acquisition and Preprocessing

By using good and high quality images, the process of noise removal and errors in calculation can be avoided. The computational errors can be avoided in the absence of reflections in the images. The images provided by CASIA are clear images and it uses Infra-red light for illuminating the eye and hence they do not involve any reflections. The iris images which are color images are converted to gray level in order to save the computational cost and storage memory.

2.2 Iris Segmentation

The first stage of iris recognition is to isolate the actual iris region in an eye image. The iris inner and outer boundaries must be located. The iris should be localized first by considering whole eye image, and later pupil is localized and removed from the iris image. In this technique, Gaussian filter is used to remove the noise and the resultant image will be a blurred image. Canny edge detection technique is then applied to the obtained smoothened image as shown in Figure 3. As a result we get the major boundaries or edges of the image.

Figure2: Basic block diagram of iris recognition system.



The effect of the canny operator is determined by width of the Gaussian kernel used in the smoothing and the upper and lower thresholds used in thresholding. If we increase the width of the Gaussian kernel, detector's sensitivity to noise will reduce, but finer detail in the image may be lost. After this process, circular hough transform was applied on the whole edge detected image.

The Hough space will find out the parameters of circles passing through each edge point which is the centre coordinates xc and yc and the radius r of iris. A maximum point in the accumulator will correspond to the radius and centre coordinates of the iris. Similarly, by taking only iris image same step is repeated, since pupil lies inside iris. Thus we obtain the radius and x and y centre coordinates for

iris and pupil. The eyelids are removed by placing a line to the upper and lower eyelid using the linear Hough transform. The eyelash is removed by thresholding.

Figure 3: Canny edge detected output. Figure 4: Segmentation output.

2.3 Normalization

The iris location and size will be different, if the acquisition environments are different, so the location and size of iris must be same, in

order to perform comparisons. So the iris image is transformed into rectangular area of fixed dimensions using rubber sheet model. The

Daugman‘s rubber sheet model remaps each point within the iris region to a pair of polar coordinates (r, Ɵ) where r is in the interval [0,

1] and Ɵ is angle ranging from [0,2π], as shown in Figure 5. The output of the normalized image is as shown in the Figure 6.

Figure 5: Normalization using Rubber sheet model.

Figure 6: Normalized image.



2.4 Feature Extraction

Iris features are obtained from curvelet transform coefficients that are obtained by applying curvelet transform on the normalized image.

The curvelet transform is a multi scale geometrical transform with frame elements as scale, location and orientation parameters. The

curvelet transform transforms the iris image into three layers sub band images. The highest frequency band contains the environment

noise and the lowest frequency band will contain the primary information.

The steps involved in the process of feature extraction are given below:

1. The curvelet transformation is applied to a normalized iris image.

2. The sub band coefficients are calculated.

3. The binary templates are formed by assigning 1‘s and 0‘s respectively, to the positive and negative quantities obtained from the

coefficients. The binary code thus obtained is called iris code, which can be used for matching purpose.

2.5 Matching Process

In the matching process, the iris code of a test image is matched against the whole data file of stored iris codes. The Hamming distance

method is chosen for matching purpose. Hamming distance gives the measure of degree of dissimilarity in two bit-patterns. It is

measured by the following expression:

H.D=1

𝑁 [Xi ⊕ Yi

𝑁

𝑖=1]. (1)

Where Xi, Yi are the two templates to be compared, N is the total number of bits present in the template.

The lowest hamming distance corresponds to the best match. Higher the hamming distance value, higher will be the dissimilarity

between the two compared bit-patterns.

3 Experimental Results

The algorithm has been implemented in Matlab. In order to evaluate the algorithm CASIA Iris-Interval database is used. The

specialized imaging conditions using near infra-red light is used and hence the features in the iris region are highly visible and there is

good contrast between pupil, iris and sclera regions. All images are stored as BMP format with resolution 320*280. The segmentation system proved to be successful. The various segmented and normalized images for Casia iris images is as shown

from figure 7 to figure 10. The iris inner and outer boundaries are clearly localized, eyelash has been detected and eyelash isolation is

performed using thresholding. The normalization of the iris region is performed, so that it has fixed dimensions in order to allow

comparisons.

Figure 7: Original image, segmented image and normalized image.



4 Conclusion

In this paper Iris Segmentation and Normalization system has been implemented using the MATLAB on Casia iris database. The boundaries of iris, pupil and sclera were clearly distinguished in casia eye images. The outer and inner iris boundaries have been localized; eyelash and eyelid isolation has been performed. The segmented images have been normalized in order to allow comparisons .In the future work iris feature extraction using curvelet transform and matching using hamming distance will be performed.

REFERENCES

1. Daugman, J: New methods in iris recognition, VOL. 37, IEEE Transaction on Systems, Man, and Cybernetics—PART B: 1083-

4419, (2007), pp. 1167-1175.

2. J. Daugman, January: How iris recognition work, Vol. 14, IEEE Trans. On Circuits and Systems for Video Technology, No. 1, (2004), pp. 21-30.

3. Casia iris image database http://biometrics.idealtest.org.

4. J. Daugman: High Confidence Visual Recognition of Persons by a Test of Statistical Independence, Vol. 15, No.11, IEEE Trans. on

Pattern Analysis and Machine Intelligence, (1993), pp.1148-1161.

5. R. P. Wildes,: Iris recognition: An emerging biometric technology, vol. 85, no. 9, Proc. IEEE, (1997) pp. 1348–1363

6. Padma Polash Paul, Md. Maruf Monwar: Human Iris Recognition for Biometric Identification, (2007), pp. 1-5.

7. Sailesh Conjeti and Dhiraj Puroshottam Jetwan: Patient Identification using High-Confidence Wavelet based Iris Pattern

Recognition, IEEE Proc. IEEE-EMBS Int. Conf. Biomedical and Health Informatics, Hong Kong and Shenzhen, China, (2012), pp.

628-631.

8. Afsana Ahamed and Mohammed Imamul Hassan Bhuiyan: Low Complexity Iris Recognition using Curvelet Transform,

IEEE/OSA/IAPR Int. Conf. Informatics, Electronics & Vision, (2012), pp. 548-553.

9. Muhammad Faisal Zafar, Zaigham Zaheer, lavaid Khurshid: Novel Iris Segmentation and Recognition System for Human Identification, IEEE Proc. 10th Int. Bhurban Conf. Applied Sciences & Technology, (2014), pp.128-131.

improved authentication using iris recognition · canara engineering college mangalore njciet 2015...

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