Person Identification by Iris Recognition Using 2-D Reverse Biorthogonal Wavelet Transform

Size: px

Start display at page:

Download "Person Identification by Iris Recognition Using 2-D Reverse Biorthogonal Wavelet Transform"

Jodie Merritt
5 years ago
Views:

1 707 Person Identification by Iris Recognition Using 2-D Reverse Biorthogonal Wavelet Transform Saloni Chopra 1, Er. Balraj Singh Sidhu 2, Er. Darshan Singh Sidhu 3 1,2,3 (Electronics and Communication Engineering (ECE) Giani Zail Singh Punjab Technical University Campus, Bathinda, Punjab, India) ABSTRACT Iris Recognition System being highly accurate and reliable is superior to all the biometric systems for person identification. This paper describes a Hough Transform and Reverse Biorthogonal Wavelet Transform based iris recognition system. Performance of the system has been evaluated on various levels of decomposition. For this, grayscale images of UBIRIS ver.1 database have been used. the iris are extracted by applying the 2-D Reverse Biorthogonal Wavelet Transform (RBWT) (rbio3.3). Hamming distance (HD) is computed for template matching [7]. Keywords- Circular Hough Transform, Decomposition, Iris Recognition, Normalization, Reverse Biorthogonal Wavelet Transform. 1. INTRODUCTION Biometrics refers to measurement of life. It is the methodology of measurement and analyses of the biological or physiological data of the living body for identification of an individual for authentication purpose. The foremost application of biometrics is security. It makes use of the unique traits of human for their identification. Iris recognition based biometric system is one of the highly promising security systems. The uniqueness and stability of iris makes the iris recognition system highly accurate. In a biometric system, the unique features of a person are extracted, stored and are used to verify or identify the person. Iris recognition system consists of various steps: Segmentation: Locating the inner and outer boundaries of iris in the eye image. Normalization: It is done in order to get the entire irises or irides in same dimensions. Moreover, it removes the inconsistency in the iris portion. Feature Extraction: Some of the unique features of the iris are extracted and are stored in a template. Secondly, decomposition of the normalized iris is done so as to reduce the size of the normalized image. Matching: Matching of the templates is done using different matching techniques like Hamming distance, Euclidean distance, Neural networks etc. Fig. 1 shows an iris recognition system. In this paper, an iris recognition system has been proposed in which the iris is localized by applying Canny Edge Detection and Circular Hough Transform [1], [4]. After that the segmented iris portion is normalized using the Daugman s Rubber Sheet Model [5]. The features of Fig. 1 Block diagram of iris recognition system The research on iris recognition based biometric system came into existence in 1990s. Iris recognition being highly reliable and accurate became a hot topic of research in biometrics for person identification. Many researchers gave various algorithms for iris recognition. Daugman was the first to give an iris recognition algorithm in 1993 and got US patent [2], [5] for the same in In this algorithm, iris boundary had been localized by applying Integro Differential Operator. 2-D Gabor Wavelet coefficients had been used for extracting the features. For comparing the two templates Ex-OR operator had been applied. Wildes et al. [1] computed gradient of intensity values and applied Hough transform to localize the iris region. Laplacian of Gaussian Filter had been used for extracting the features and Normalized Correlation between the two iris templates differentiated the irises. A zero crossing representation of 1-D Wavelet Transform was obtained by Boles et al. [3] and matching was based on the two dissimilarity functions. Lim et al. [8] performed 4-level decomposition using Haar Wavelet Transform and Neural Network had been used for matching. In [9], a 1-D Morlet Wavelet Transform based algorithm had been

2 708 introduced. AND operation had been applied for template matching. 1-D Log Gabor Wavelet based iris recognition algorithm was introduced by Zhou et al. [10]. For searching, K-D tree had been employed. In [7], Mohd. T. Khan et al. proposed an algorithm in which iris boundaries had been localized by computing the window vectors and used 1-D Gabor Filter for feature extraction. This paper is divided into 4 sections. Proposed work has been detailed in section II. Section III gives the result evaluation of the proposed methodology. Finally it is concluded in session IV. 2. PROPOSED ALGORITHM Proposed iris recognition algorithm is described in this section. The localization of the upper and lower boundaries of iris has been obtained by applying Canny Edge Detection (CED) and Circular Hough Transform (CHT) [4], [1]. After that the segmented iris portion has been normalized into fixed dimensions by using Daugman s Rubber Sheet Model [5]. For extracting the iris features used 2-D RBWT and stored them in a template. The matching has been performed by computing the Hamming distance between the two templates. Fig. 2 shows the block diagram of the proposed algorithm. Th. in Fig. 2 represents the threshold value. Range of pupil circle : 5-20 Range of iris circle : Canny Edge Detection: It is the most effective edge detection technique. An Edge map of the eye image is obtained after applying CED. It can be divided into five steps as follows: Step I. Gaussian filter: The Gaussian filter is applied on the input image so as to suppress the high frequency signals. This is done by convolving the input image with the Gaussian function with a specified value of sigma. In the proposed algorithm the sigma has been taken as 2. (1) shows the Gaussian function with a standard deviation. Gaussian function = e x2 +y 2 2 σ 2 (1) After applying the Gaussian filter, a smoothened image is obtained. Step II. Gradient operator: The first order partial derivatives of the image are obtained by implementing gradient operator on the smoothened image. Gradient magnitude and orientation are computed according to (2) and (3) respectively. P = P x 2 + P y 2 (2) θ = tan 1 (P y + P x ) (3) In (2), P x shows the change in the intensity values of the pixels of rows. P y shows the change in the intensity values of the pixels of columns. P shows the combine change in the horizontal and vertical directions. Fig. 2 Block diagram of the proposed algorithm The proposed methodology is discussed below: 2.1 Segmentation Segmentation is the process of localizing the iris portion i.e. the area between the pupil and the sclera (white portion of the eye). CED and CHT [15] have been applied to localize the iris in the eye image. For localization, following parameters have been selected: Step III. Adjacent gamma: All the dominating as well as weak edges of the input image are obtained after implementing Gradient operator. In order to remove the weak edges, Adjacent gamma is applied. This gives the dominating edges of the image as shown in Fig. 3(b). The value of gamma in the proposed algorithm has been kept as 1.8. a) b)

3 709 c) d) Fig. 3 a) Original image b) Output of adjacent gamma c) Output of NMS d) Output of hysteresis thresholding Step IV. Non maxima suppression (NMS): Gradient gives wide ridges all around the local maxima. So, thinning of these edges is required which can be obtained by applying some edge thinning technique. In the proposed algorithm NMS has been used for suppressing or omitting the non maximums. In Non maxima suppression, one of the pixel values is selected as local maxima. The pixels having intensity values lesser than the local maxima are set to black and others are kept unchanged. Result of non maxima suppression is shown in Fig. 3(c). Step V. Hysteresis Thresholding: The final step of the CED is to remove the false edges in the image. This is done by thresholding the output of the NMS. Two threshold levels are set. Output obtained after thresholding with the upper threshold contains the true edges with gaps and no false edges are there. The lower threshold gives false edges along with the true edges as its output. The gaps in the output of the upper threshold are filled with the information provided by the lower threshold. Hence an edge map with true edges is obtained. Edge map obtained after hysteresis thresholding is as shown in Fig. 3(d). The values of upper threshold and lower threahold have been taken as 35 and 32 respectively, in the proposed algorithm Circular Hough Transform: Now to obtain the contours of iris and pupil, CHT has been applied. CHT gives the circumference of the iris and the pupil as per (4) [4]. x 2 2 c + y c = R 2 (4) (x c, y c ) represents the center coordinates of the circle and R is the radius. 2.2 Normalization After the localization of the iris and the pupil in the eye image, normalization has been performed using Daugman s Rubber Sheet Model [5]. Fig. 4 shows the Daugman s rubber sheet model. In this the Cartesian coordinates (x,y) are converted into polar coordinates (R, ). Fig. 4 Daugman s rubber sheet model The iris portion after normalization became rectangular in shape with fixed sizes for all the iris images. Thereafter, the region of interest has been obtained. The normalized image is shown in Fig. 5(a). After achieving normalization, region of interest is obtained by eliminating the iris portion that is occluded by the upper and the lower eyelids. Then the image is enhanced by the application of Gaussian filter. Fig. 5 (b) and (c) shows the resultant image for region of interest and enhanced normalized image. d) Fig. 5 a) Normalized image b) region of interest c) enhanced image d) Extracted features a) b) c) 2.3 Feature extraction After that, the features of the normalized iris portion are extracted. In the proposed algorithm, the iris features has been extracted using wavelet transform. Multi level decomposition using 2-D RBWT has been performed. The extracted features get stored in the coefficients of the wavelet transform. In case of the multi level decomposition, the next level of decomposition is performed on the coefficients of the previous level. Then the binarization has been done. After decomposition, the size of the image gets reduced. The dimensions of the normalized image were The dimensions became 34 9 after decomposition. The extracted iris features were stored in a template. Fig. 5(d) shows the output of the feature extraction.

710 2.4 Template matching For template matching, Hamming Distance calculation has been chosen. A Threshold (Th.) level is set.

If the HD is more than the threshold, the user is an unauthorized person. The HD in the proposed algorithm has been computed as per the (5). HD = A xor B total length of B 3.

4 Template matching For template matching, Hamming Distance calculation has been chosen. A Threshold (Th.) level is set. HD between the user template and the templates stored in the database is computed. If the HD is less than the threshold, then the user is considered to be an authorized user. If the HD is more than the threshold, the user is an unauthorized person. The HD in the proposed algorithm has been computed as per the (5). HD = A xor B total length of B 3. RESULT ANALYSIS MATLAB R2012a is used for implementing the algorithm. The proposed algorithm has been tested on 400 grayscale images taken from the UBIRIS ver.1 database. 200 images from session 1 and 200 from session 2 were taken for the evaluation of the algorithm. Total 50 subjects and four eye images of each subject were taken from session 1 and session 2. 95% and 53.5% segmentation results have been achieved for session 1 and session 2 respectively. 39,200 inter class and 300 intra class Hamming distances have been computed. First six levels of decomposition of 2-D RBWT were implemented and 5- Level decomposition was found to give the best results in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR) and Correct Recognition Rate (CRR). CRR has been computed by (6). CRR% = 100 FAR %+FRR % 2 At a Th. of 0.365, for session 1, FAR of 0.22%, FRR of 5.33% and CRR of % have been achieved with 5- level decomposition of 2-D RBWT. For session 2, 0.24% FAR, 25.00% FRR and 87.38% CRR have been obtained. Table I shows the FAR, FRR and CRR of first six levels of decomposition of 2-D RBWT. (5) (6) d) e) f) Hamming Distance (x-axis) versus frequency ( 10 3 ) (yaxis) graphs for session 1 and session 2 images are shown in Fig. 6. The graphs shows the overlapping of Hamming distance of inter and intra class images when 5-level decomposition of 2-D RBWT has been applied. Frequency here signifies the number of iris images. a) Table I a) Level 1 b) Level 2 c) Level 3 d) Level 4 e) Level 5 f) Level 6 a) b) c) b) Fig. 6 a) Overlapping graph for session 1 b) overlapping graph for session 2 4. CONCLUSION This paper implemented an iris recognition algorithm with a new approach which includes 2-D Reverse Biorthogonal

5 711 Wavelet Transform used for extracting the iris features. The proposed algorithm gives a high correct recognition rate with low FAR and FRR. Comparison of proposed algorithm with some well known algorithms is shown in Table II. It is clear from Table II that the performance of the proposed algorithm is better than Mohd. Tariq Khan et al. algorithm [7] and M. Sundaram et al. [6] algorithm. Daugman [2] and Boles et al. [3] evaluated the performance of the algorithm on noise free images. Whereas, noisy image database has been taken for the performance evaluation of the proposed algorithm. So, it is concluded that the proposed algorithm is an efficient iris recognition algorithm. ACKNOWLEDGEMENT We would like to express our deep gratitude to Dr. J. S. Hundal for his valuable support. We are thankful to Dr. Jyoti Saxena for her guidance. We are thankful to Er. Rajvir Singh for his guidance and motivation at every step. Table II Comparison of proposed algorithm and pre existing algorithms. REFERENCES [1] R. P. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride, Automated, noninvasive iris recognition system and method, U.S. Patent A, Nov 5, [2] John G. Daugman, Biometric Personal Identification System based on Iris Analysis, U.S. Patent no A, March 1, [3] Boles and Boashash, A Human Identification Technique Using Images of the Iris and Wavelet Transform, IEEE Transactions on Signal Processing, vol. 46, no. 4, pp , [4] L. Masek, Recognition of human iris patterns for biometric identification, Technical Report, School of Computer Science and Soft Engineering, The University of Western Australia, [5] John G Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, no.11, pp , [6] R. Meenakshi Sundaram and Bibhas Chandra Dhara, Neural Network Based Iris Recognition System using Haralic Features, 3 rd International Conference on Electronics Computer Technology, vol. 3, pp.19-23, [7] Mohd. Tariq Khan, Dr. Deepak Arora and Shashwat Shukla, Feature Extraction through Iris Images using 1-D Gabor Filter on Different Iris Datasets, 6 th International Conference on Contemporary Computing, pp , Aug [8] S. Lim, K. Lee, O. Byeon, T. Kim., Efficient iris recognition through improvement of feature vector and classifier, ETRI Journal, vol. 23, no. 2, pp , [9] Zhonghua Lin and Bibo Lu, Iris Recognition Method Based on the Imaginary Coefficients of Morlet Wavelet Transform, 7 th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, pp , [10] Steve Zhou and Junping Sun, A Novel Approach for Code Match in Iris Recognition, 12 th International Conference on Computer and Information Science, IEEE/ACIS, pp , [11] Bimi Jain, Dr. M. K. Gupta and Prof. Jyoti Bharti, Efficient Iris Recognition Algorithm Using Method

6 712 Of Moments, International Journal of Artificial Intelligence & Applications, vol. 3, pp , [12] Marry Dunker, Dont Blink: Iris Recognition for Biometric Identification, SANS Institute InfoSec Reading Room, [13] B. Thiyaneswaran, S. Padma, (2012), Iris Recognition using Left and Right Iris Feature of the Human Eye for Bio-Metric Security System, International Journal of Computer Applications, vol. 50, no. 12, pp [14] MATLAB user s guide, The Mathswork, Available: [15] R. C. Gonzalez and R. E. Woods, Digital signal processing, third edition. [16] Qin Zhao, A New Approach for Noisy Iris Database Indexing Based on Color Information, 6 th International Conference on Computer Science and Education, pp , [17] F. R. J. Lopez, C. E. P. Beain, O. E. U. Mendez, (2013), Biometric Iris Recognition Using Hough Transform, 18 th Symposium of Image, Signal Processing and Artificial Vision, date of conference Sept, 2013, pp. 1-6.

Robust IRIS Recognition System based on 2D Wavelet Coefficients

Available online www.ejaet.com European Journal of Advances in Engineering and Technology, 215, 2(7): 43-47 Research Article ISSN: 2394-658X Robust IRIS Recognition System based on 2D Wavelet Coefficients