Feature Extraction through Iris Images using 1-D Gabor Filter on Different Iris Datasets

Mohd. Tariq Khan

ASET, Amity University, Lucknow Department of Computer Science &

Engineering Lucknow,India

khantariq1987@gmail.com

Dr. Deepak Arora ASET, Amity University, Lucknow

Department of Computer Science & Engineering

Lucknow,India deepakarorainbox@gmail.com

Shashwat Shukla ASET, Amity University, Lucknow

Department of Computer Science & Engineering

Lucknow,India shashwatshukla10aug@gmail.com

Abstract— In recent years the human iris has established itself as a robust biometric. Biometric deals with identification of individual person based on their behavioral characteristic. Iris recognition is one of the most reliable and widely used biometric techniques available. This paper describes the iris recognition system using 1-D Gabor filter. Author describes a customized iris feature extraction algorithm which has been applied directly on the iris images. 1D-Gabor is used to extract the unique features which include real and imaginary part. The proposed method uses these features to recognize individual’s identity on different sets. Hamming distance based matching algorithm is used for iris template matching. The proposed algorithm has been tested on different iris database with noisy image and noise free image and the result showed the excellent accuracy with excellent processing speed.

Key words—1-D Gabor filter; Iris recognition; hamming distance

I. INTRODUCTION Biometrics involves recognizing individuals based on their

physiological and behavioral characteristic. Biometric systems provide reliable recognition schemes to determine the individual identity. Iris is the internal body organ that is very much visible from outside world but well protected from external threats. It has the unique features which remains same from birth to death. Two eyes from the same individuals although are very similar contain unique patterns. These unique characteristic is used as a biometric feature to identify individuals. Image processing algorithms can be used to extract the unique feature of iris and these form the iris code. The technology combines comport vision, pattern recognition, statistical inference, and optics.

Purpose of the algorithm is real-time and gives high confidence recognition of individual identity by mathematical analysis of the unique features random patterns that are available. Iris is very well protected by human internal organs where texture is complex, unique and very stable throughout life it can serve as a unique identity that one carry from birth till death. The algorithm for iris recognition were developed at Cambridge university bt John Daugman. Human iris recognition process is basically divided into several steps:

Localization: Inner and outer boundaries of the iris are detected. Normalization: It is performed to get all the images in standard form suitable for processing. Feature extraction: Iris has abundant texture information, a feature vector is formed which consist of unique sequence of features extracted from the iris image. Matching feature: Different technique like hamming distance, weight vector, winner selection and dissimilarity function etc are applied. The result is a set of complex numbers that carry information. In Daugman’s [14] algorithm all amplitude information is discarded and the resulting bits consist only of the complex sign bits of the Gabor domain representation of the iris image.

Fig. 1. Human Eye Structure [3]

We are performing testing on database, UBIRIS v1 [11], CASIA- Iris Twins [7], IIT DELHI DATABASE [2]. We found good recognition rate with FRR% and FAR% on different database, which contain twin iris, noisy datasets and noise free datasets.

II. RELATED WORK Daugman first proposed an algorithm for iris recognition.

His work is based on iris codes. Operators are used to detect the center and boundaries of iris. Iris Features extraction algorithm uses the 1D Gabor filter to extract the unique iris codes which are then compared using Hamming distance. This algorithm is tested on different type database and gets the accuracy of more than 99.99% [12-13]. Author proceed to extract the set of one dimensional(1-D) signals and get the zero-crossing representation based on its dyadic wavelet transform[19].

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Author have used (SIFT) for recognition. This approach does not rely on transformation of the iris pattern to polar coordinates [4]. Basically SIFT features are extracted matching based on the textural information around the features part using SIFT operators. Iris features are extracted by ICA to recognize iris pattern. PCA is a dimension reduction tools that reduces large variables to small that still contains the most of the information [18]. Wildes suggested iris recognition method using Gaussian filter of decomposing iris images in different resolution [20-21]. Like Daugman, Wildes also used the first decomposition of image to find the location of edges for the iris [17]. Wildes models the upper and lower eyelids, whereas Daugman excludes the upper and the lower portion of the image. Boshash. and Boles gave some new approach based on zero crossing. First they localized then normalized the iris by using edge detection algorithm then zero crossing of the wavelet transform is calculated at various resolution levels. The systems can handle noisy datasets [10]. Dargham used the threshold value to detect iris. Now the detected iris is converted into rectangular format. Accuracy obtained in this method is around 83% [8]. Li ma et. Al, multichannel used circular symmetry filters which capture the texture information of iris, which is then used to construct a fixed length vector. Nearest feature line method is used for iris matching with 0.01% for false match rate and 21.7% for false non-match rate [15-16]. Chen and Yuan used fractal dimension for extracting the iris features, it is zone is divided into small blocks and the extracted features and stored as iris codes. Now matching is done through K-means and neural networks, results obtained are 91.8% acceptance of authentic person and 100% rejection rate for fakers [6]. Robert et al. introduced new algorithm for localization and extraction of unique iris features. For localization Hough transform is used and iris codes are extracted by using emergent frequency which gives real and imaginary part. Hamming distance is used for matching FRR is 11% [5].

III. PROPOSED METHODOLOGY In this section, the proposed method for iris recognition is

discussed. The method proceeds as follows. The algorithm is proposed for iris identification and tested on different datasets. Image is taken as input after that iris is detected then segmentation and localization is performed. 1D Gabor wavelet features are extracted. Hamming distance is used to match iris image

Phase I: In phase Ι image is taken as input all the basic steps are performed which includes image segmentation, localization, feature extraction. In our algorithm we have extracted the Gabor Features.

YES NO Phase II: In phase ΙΙ Hamming distance is calculated for the comparison. If Hamming distance = 0 then the result authenticated else rejected. NO ES

YES

Fig. 2. System Overview

A. Pre-processing

UBIRIS v1, [11], CASIA-IRIS TWINS [7], IIT DELHI

IRIS DATABASE [2]

Pre-processing, image segmentation and localization

Serialize into database

Calculate the hamming distance of the user to verified

Extraction of 1D Gabor feature.

If more user to add

Compare the users hamming distance with stored templates

If hamming distance=0

Input image

Result authenticated

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The characteristic of UBIRISv1 [11] is to incorporate images with several noise factors, simulating less constrained acquisition environment. In first session noise factor are minimized. In the second session the capture place is changed in order to introduce natural luminosity factor, which includes reflections, contrast, luminosity and focus problem. Images were taken from annual twins festival in Beijing, even twins have their unique patterns, it is interesting to study the dissimilarity and similarity between the iris images of twins from CASIA-IRISTWINS [7]. In IIT DELHI DATBASE [2] images consist of iris image collected from the students and staff of IIT-DELHI.

.

(a)

(b) (c)

Fig. 3. Example of different IRIS database (a) CASIA-IRIS Twins (b) UBIRIS v1 (c) IIT DELHI IRIS image

1) Iris Segmentation Our database contains iris images which includes noisy

image and noise free images. The assumption for segmentation process is that the pupil color is very much different from iris and iris color different from sclera. So we can easily distinguish pupil, iris and sclera. Detecting Pupil-To find the pupil a threshold is applied on the eye image. Threshold value varies between {0,1}. We get different threshold values for different image. Pixels with intensity less than a specified value are converted to 0 (black) and greater or equal are given 1(white). Freeman’s chain code algorithm to extract the pupil region [1]. At last the edges of the pupil are obtained by two imaginary orthogonal lines passing through centroid of the region.

2) Iris Localization Steps taken to detect the edges of the iris image ,

a) Center of pupil , radius are derived by pupil detection algorithm

b) Apply linear contrast filter on image , to obtain linear contrast image , .

c) Create vector T={T1,T2…Tw} that holds pixels intensities of the imaginary row passing through the center of pupil w width.

d) A vector R is created from vector T which contains elements of T starting from the fringe to right most element of T. Similarly vector L is created starting from left fringe of pupil and ending at left most element of T.

e) From both side of the pupil (vector R and vector L).

• Calculate the average window vector.

• Locate the edge point for both the vector L and R as the first increase of value in average window vector as the first increase of values in average window vector that exceeds a threshold t.

Thus pupil, iris center and radius are obtained and the

circle is drawn using these value to find pupil and iris edges.

B. Feature Extraction In our system unique feature information from the iris is extracted by the 1-D Gabor filter. To understand the concept of Gabor filter it is necessary to understand Gabor wavelet. , = , , …(1) Complex carrier in the form: , = ..(2) We extract the real and imaginary part. Real part: , = cos 2 ...(3) Imaginary part: , = sin 2 ..(4)

: Frequency of horizontal sinusoidal. : Frequency of vertical sinusoidal.

: Arbitrary phase shift. The second component is its envelope. The envelope has a Gaussian profile and described as

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Fig. 4. Shows the Gaussian Profile.

, = + Where cos

sin

The parameter used above:

-Scaling constant , - Envelope axis : Envelope rotation constant. , - Gaussian envelope peak.

We multiply , ,

Fig. 5. Shows the 1D Gabor Wavelet.[

I-D Gabor Filter technique extract the uniqufrom the iris and then store them. These uniknown as iris code. 1D Gabor Filter is usedfilter is complex, which gives real and imaginFigure 4(b) (c) shows the real and imaginary p

[9]

.. (5)

sin …(6)

cos ...(7)

[9]

ue set of features ique iris features d because Gabor nary parts .In the parts.

(a)

(c)

(e)

(f) Fig. 6. (a) Input iris image (b)[17] and (c)[17segmented iris image (e) normalized iris por

C. Hamming Distance ComparisonThis phase consist mainly of

features by hamming distance proceThe new image is compared with evand if the hamming distance is foimage is identified and accepted other than zero is unidentified andistance approach is a matching Daugman for comparing and it repthat are different. Another matchingweighted Euclidean distance wcalculation and this metrics also values. Normalized correlation involves computation. Hamming dsimple. Basically the proposed algdifferent database like noisy imagefree images. An attempt has been made to measbetween two iris codes. Hamminbetween binary vectors by the ddivided by the length of vectors. vectors have distance 0 and differother than zero.

(b)

(d)

7] the real and imaginary part (d) rtion (f) segmented iris portion.

n two steps, matching the ess and then identification. very image in the database

ound to be 0 then the iris else if hamming distance

nd rejected. The hamming technique developed by

presents the number of bits g metric that can be used is which involves lots of

involves lots of integer matching technique also distance is more fast and gorithm is been tested on es, twins images and noise

sure the hamming distance ng distance is calculated difference in bit position

These way two identical rent vectors have distance

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D = A B Length (A) [9]

TABLE I

In the above table for matched iris templates the hamming

distance is zero. Now for unmatched iris templates the hamming distance is non zero. Hamming distance values at various pixels are different for different database. Maximum and minimum represents the values of hamming distance at various pixels. In the above table 1 hamming distance for matched iris template and unmatched iris template is shown.. Even twins have different iris patterns. Now for unmatched iris templates the hamming distance is not close to zero, the maximum points are far away from zero. The recognition rate for the Twins database is 99.47%. Hamming distance for the matched iris is zero. Recognition rate for the IIT Delhi iris database is 100%.FAR% and FRR% is zero. The proposed algorithm is very accurate and very fast in processing. The unmatched iris templates are far away from zero. UBIRIS v1 is composed of large amount of database in two distinct sessions. In first session noise factors especially those relative to reflections, luminosity and contrast were minimized. The accuracy (Accuracy = 100 – (FAR+FRR)/2) of UBIRISv1 session 1 is 93.445%. At excellent processing speed is achieved.

(b)

(a) (c)

Fig 6: In the above figure (a) noisy iris image (b) normalized iris portion and (c) segmented iris portion.

In the second session the capture place is changed in order to introduce luminosity factor. The accuracy (Accuracy = 100-(FAR+FRR)/2) of UBIRISv1 session 2 is 83% at excellent processing speed is achieved.

IV. EXPERIMENTAL SETUP To conduct the experiment authors have used different datasets, specifically from UBIRIS v1, IIT DELHI DATABSE and CASIA-IRIS TWINS. MATLAB R2008b is used for implementing the proposed algorithm. In this experiment iris images from these databases are used for generating iris templates. Now set of these iris templates are getting compared with already stored iris templates. Table 1 and Table 2 shows the experimental results on various data sets,

TABLE II

V. EXPERIMENTAL RESULTS The proposed algorithm are tested on the UBIRISV1, CASIA IRIS TWINS, IIT DELHI IRIS DATABASE,. Testing is performed on 200 iris image in each case. The iris detection process shows good performance and computation speed. CASIA TWINS database shows FRR 0.05% and FAR 0% at a very good recognition rate. IIT Delhi database is noise free image database with zero FAR% and FRR% and nearly 100% recognition. We also performed experiments on UBIRISv1 session 1 and session 2. Session1 has less noise factor so we got 3.87% FAR and 9.29% FRR and recognition rate 93.445% for authentication of a person at excellent processing speed. Session 2 has much more noise factor we found 23.15% FAR and 23.00% FRR with overall success of 83%, which gave excellent speed. Both FAR and FRR for noise free dataset is achieved 0.00%. The above experiment is performed on MATLAB R2008b. The performance of biometric system is calculated using false acceptance (FAR) and false rejection (FRR). Table 2 shows different FAR and FRR for different subjects. Result shows that if more subjects are considered then error rate increases.

CONCLUSION AND FUTURE SCOPE In this paper a fast and effective algorithm is proposed for the extraction of feature by 1 D Gabor filter normalizing and segmenting the iris and pupil boundaries of the eye from database images. Center and boundaries are quickly detected even in the presence of eyelashes, noise and even in presence excess illumination. For comparing we have one of the best techniques hamming distance which shows excellent processing speed. This algorithm can serve as an essential

FOR MATCHED IRIS TEMPLATE HAMMING DISTANCE ARE:

FOR UNMATCHED IRIS TEMPLATES HAMMING DISTANCE ARE:

CASIA IRIS-TWINS DATABASE

. 0.00

min. max.

0.2681 0.4895

IIT DELHI DATABASE

0.00 0.1667 0.4555

UBIRIS V1 SESSION 1

0.00 0.3191 0.5027

UBIRIS V1 SESSION 2

0.00 0.2419 0.5596 TWINS DATABASE

UBIRISV1

Session1 Session2

DATABASE 200 200 200 FAR (%) 0.00 3.87 23.15 FRR (%) 0.05 9.24 23.00

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component of iris recognition applications. Now we are working to increase the database in order to get more performance.

ACKNOWLEDGEMENT We would like to thank respected Mr. Aseem Chauhan, Chairman, Amity University, Lucknow and Maj. Gen. K.K. Ohri AVSM (Retd.), Pro.Vice-Chancellor, Amity University, Lucknow for providing excellent facilities in university campus and their encouragement and advice. We would also like to pay regards to Prof. S.T.H. Abidi, Director and Brig. U. K. Chopra, Deputy Director, Amity School of Engineering & Technology, Amity University, Lucknow for their valuable feedback.

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