Assignment Pattern v2

8/10/2019 Assignment Pattern v2

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Assignment 2Application of pattern recognition in

biomedical engineering

Iris Recognition

Lecturer

Dr. L iew Yih Miin

Group members:

Foo Yoke Yin KEU 110010

Lai Eng Seong KEU110012

Lee Kar Mun KEU110013

Lim Su Yi KEU110015

Vivian Koh Ci Ai KEU110047

Yong Ching Wai KEU110049


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Table of Contents

1.0 Introduction ................................................................................................................... 2

2.0 Iris Recognition Implementation ................................................................................... 4

2.1 Combination of Support Vector Machine and Hamming Distance Approach .......... 4

2.1.1 Technical Implementation ................................................................................. 5

2.1.2 Results ............................................................................................................... 9

2.1.3 Discussion ........................................................................................................ 11

2.1.4 Conclusion ....................................................................................................... 11

2.2 Neighborhood-Binary Pattern (NBP) Approach ...................................................... 12

2.2.1 Technical Implementation ............................................................................... 12

2.2.3 Result ............................................................................................................... 17

2.2.4 Discussion ........................................................................................................ 17

2.2.4 Conclusion ....................................................................................................... 17

3.0 Overall Discussion ........................................................................................................ 18

4.0 Conclusion ................................................................................................................... 20

5.0 Future Outlook ............................................................................................................ 20

6.0 Reference .................................................................................................................... 21


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1.0

Introduction

With the advent of technology, biometrics authentication is made possible in

determining human identity. Biometrics authentication is a combination of using both

the computer system and human biometrics, the physical or behavioral traits such as

fingerprints, face, palm prints, hand geometry, iris and voice in identification. Among

the traits, iris has particularly gain the interest of mankind to use it as a recognition

system due to its rich texture which offers a strong biometric cue for recognizing

individuals (Ross, 2010).

Figure 1: The Anatomy of Eye

The iris is the color portion of the eye which located behind the cornea and in front of

the lens. It uses the pupillary dilator and sphincter muscles that govern the pupil size

to control the amount of light enters the eye. The details and pattern of the iris for

everyone is different due to the way it develops. During prenatal growth, iris develops

through a process of tight forming and folding of the tissue membrane. Prior to birth,

degeneration occurs causing the pupil to open along with the formation of the random

and unique patterns of the iris. Although the correlation and structure of the iris is

genetically linked, each individuals irises are unique and structurally distinct

(Westmoreland, Lemp & Snell, 1998).


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Therefore, pattern recognition is one important stage in iris identification. Through

recognizing the patterns on the iris, one individual iris can be distinguished from one

another. It is impossible for man to recognize the pattern of human iris and identify

the owner from it. Pattern recognizing system can match observed iris patterns to

stored patterns, recognizing iris patters through its features and perform structural

analysis of features to identify their correlations. Hence, a pattern recognition system

plays a very important role to an extend such that, without this the development of iris

identification will halt.

The concept of using iris as a method in recognition is proposed by ophthalmologist

Frank Burch in 1936. Following in 1985, Drs. Leonard Flom and Aran Safir,

ophthalmologists, proposed the concept that no two irides are identical, and were

awarded a patent for the iris identification concept in 1987. Dr. Flom approached Dr.

John Daugman to develop an algorithm to automate identification of the human iris.

In 1993, a prototype unit had successfully completed by the Defense Nuclear Agency

with the help from Drs. Flom, Safir and Daugman. Dr. Daugman was then awarded a

patent for his automated iris recognition algorithms in 1994 and the first commercial

products become available in 1995. In 2005, the broad patent covering the basic

concept of iris recognition expired, providing marketing opportunities for otherparties to develop their own algorithms for iris recognition.

Iris recognition method have been intensively applied for identification purposes. For

example, India is using it for Unique ID program, The United Arab Emirates is using

for border control, so do airports in London Amsterdam and elsewhere to speed up the

process of identification. Nevertheless, there are still complex challenges faced when

special conditions appear, such as the wearing of contact lenses, artificial eyes,

accidental damaged on iris and all sorts of biological conditions related to the iris.

More development has to done to overcome these flaws (Burge & Bowyer, 2013).


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2.0 Iris Recognition Implementation

2.1 Combination of Support Vector Machine and Hamming

Distance Approach

In the old time, only one method will be used for iris recognition. For example, the

frist algorithm for iris location was first proposed by Daugman in 1993. Then

researcher had found out that Hamming distance can be used for matching purpose

and an alternative segmentation method (Wildes, 1997) in which edge detection

operator and Hough transform were used. The upper and lower eyelids will be

explicitly modelled with a parabolic arcs. The method in recognizing iris is keep on

improving and researching by researchers. However, a combination of two

classification methods (Rai, 2014) were proposed. The zigzag collarette area of iris is

chosen as feature extraction due to its complex pattern (Roy & Bhattacharya, 2006).

Then parabola detection technique will be used to detect eyelid and eyelash will be

removed by using median filter. HAAR wavelet and 1D Log Gabor filter will be used

for feature extraction. Then support vector machine will be used as main classifier

followed by Hamming distance. By combining two methods of support vector

machine and Hamming distance approach, the accuracy on CASIA and Check iris

database can be increased if compared to single method.


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2.1.1 Technical Implementation

Iris image capture

Segmented Zigzagcollarette area

Eyelid detection

Normalization

Feature Extraction

ClassificationEnrolled

Database

Result


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Step 1: Localization of iris boundary

Hough transform is a commonly used technique for identifying the parameters of

simple geometric objects, including lines and circles present in a given image. Hough

transform calculates the centre and radius coordinates of the iris region (Masek,

2003). The parametric equations for a circle with radius (r) and centre (xc, yc) are

stated as following:

x = xc+ r cos (1)

y = yc+ r sin (2)

The points (x, y) give the perimeter of a circle when angle sweeps through the entire

360. The radius can be set as a constant so that the parametric representation of the

circle can be simplified. Edge detection technique can be applied before adopting

circular Hough Transform to find the circles in the given image.

Hough transform with all edge points (xi , yi) where i = 1, 2,... n can be written as

following:

Note: The highest coordinates (xc, yc, r) is selected as the coordinate of centre and

radius of the circle.

Step 2: Selection of zigzag collarette area

The selected zigzag collarette area provides important iris features as most complex

patterns of iris are captured in this area. Zigzag collarette area is unlikely affected by

eyelids and eyelashes due to its close position with the pupil.

Step 3: Eyelid detection

Upper and lower eyelids detection requires two search regions that are confined

within the zigzag collarette area of the iris and pupil. The width of search region can

be determined by following equation:


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Width of search region = radius of irisradius of pupil (5)

The edge image of an eye image can be determined using horizontal edge map due to

the presence of eye lids in upper and lower horizontal region. Eyelids detection is

done by adopting parabolic Hough transformation at each edge point within the search

Step 4: Normalization

Right after a successful segmentation, transforming the iris region will be done so that

it has fixed dimensions. Normalization process is done by using rubber sheet model

devised by (Daugman, 1993). After that, eyelash removal method will be used to

remove eyelashes and restore the underlying iris pattern as much as possible by

recreating Zigzag collarette area pixel that occluded by eyelashes. This can be done

by using the information from their non-occluded neighbors. We need to decide if

each of the pixel present in normalized image is occluded by eyelash. The equation

below can be used to determine the occlusion by eyelash.

If I(x,y) < T, pixel is occluded by eyelash. (6)

whereIis the intensity of a pixel, Tis threshold.

If the pixel satisfies equation (6), then a 5 x 5 median filter will be applied on that

particular pixel. In 5 x 5 neighborhoods, only pixels that are greater than Twill be

chosen as it is not occluded by eyelash. Lastly, all the pixels will be sorted in

ascending order and center pixel value will replace with median value of 5 x 5

window neighborhoods.

Step 5: Feature Extraction and Matching

The relevant texture information needs to be extracted after normalization has done.

Two methods to extract feature are proposed, ie Haar wavelet decomposition and 1D

Log Gabor wavelet. In the other hand, there are also two classifiers will be used

which are the support vector machine (SVM), as the main classifier, and Hamming

distance, as the second classifier. For data analysis in iris region in under multi-

resolution mode, wavelets have the advantage over the traditional Fourier transform

as the frequency data is localized in wavelet, allowing feature which occur at the same

position and resolution to be matched up. HAAR wavelet is applied to the normalized

image of 64 x 512 at three different levels successively for feature extraction. The


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HAAR wavelet transform will be performed repeatedly in order to reduce information

sizes.

By convolving the normalized iris region with 1D Log-Gabor wavelet, the feature

encoding can be implemented. The 2D normalized iris region will first be broken into

a number of 1D signals and then these signals are convolved with 1D Gabor wavelets.

In order to prevent the influence of noise in output, the intensity values at known

noise area in the normalized pattern are set to the average intensity of surrounding

pixels. The encoding process will produce a noise mask along with bitwise template

which will be further used for classification purpose. Combined Support Vector

Machine and Hamming distance based classification approach is applied on each iris

image for matching purpose. 512 most important features of a given iris are extracted

using HAAR wavelet. The training and testing of SVM is done using the extracted

features. Hamming distance is applied for correct classification. If the classification is

done by SVM, n of SVM models will be developed for n classes present in the

training phase. When the training phase is finished, the given iris image will be tested

against all n SVM models for iris recognition. The evaluation of SVM performance is

determined using false acceptance rate (FAR) and false rejection rate (FRR). FAR is

defined as the probability of identifying an outsider as an enrolled user. On the otherhand, FRR is the probability of rejecting an enrolled user (wrongly recognizing

enrolled user as outsider).

Hamming distance is applied if the given iris is falsely rejected. 1D Log Gabor filter

is adopted to extract 2048 bit feature before applying Hamming distance for

classification. Hamming distance is calculated as mean difference between the given

iris and training templates for each class. For instance, if there are 5 training templates

in a particular class, then hamming distance S is calculated by following equation:

Si=

for class i = 1, 2, , n

User authenticity is determined by comparing the mean Hamming distance with a

threshold value.

True, if S


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2.1.2 Results

Figure 2: Selection of zigzag collarette area

Figure 3: Eyelids detection

Figure 4: Normalization process

Figure 5: Eyelashes removal


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Figure 6: Matching process

Table 1

Table 2


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2.1.3 Discussion

There are several reasons of applying two different techniques for feature extraction

and classification. The reasons are as following:

1. Information about the noise mask could not be obtained by Haar decomposed

feature vector. This information plays a significant role in Hamming distance

classification. Thus, Haar is not suitable for Hamming distance classifier but

SVM classification.

2.

The feature vector obtained by using 1D Gabor wavelet is suitable for

Hamming distance but not for SVM.

3.

According to extensive testing of different networks, combination of SVM andHamming distance approach will give a better recognition accuracy than using

a single method. As SVM alone has relatively high false rejection rate,

Hamming distance is added to further overcome this problem, resulting in

higher recognition rate.

Based on the Table which demonstrate the accuracy comparison between the

proposed method and previous reported approaches, combined SVM and Hamming

distance method achieved 99.91% of accuracy, which ranks the highest among all the

approaches.

2.1.4 Conclusion

An efficient approach for iris feature extraction and recognition can be done by using

the method as discussed above. Zigzag collarette area of iris is chosen for the feature

extraction due to its ability to capture the most significant area from the complex

pattern of iris and hence a higher recognition rate can be achieved. HAAR wavelet

and 1D Log Gabor filter are used to extract feature which will then being used in iris

identification using the combined support vector machine and Hamming distance

approach. Parabola detection and trimmed median filter are also been used to detect

eyelid and eyelash. Comparisons among previous reported approaches and the one

currently proposed were made. It indicates that the recognition accuracy is higher

when using combination of SVM and Hamming distance if compared to either SVM

or Hamming distance. Accuracy in terms of FAR and FRR for this method is


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exceedingly high for the CASIA database. In short, this proposed approach not only

efficient in case of identification but also verification.

2.2 Neighborhood-Binary Pattern (NBP) Approach

A novel new feature extraction method known as Neighborhood-Binary Pattern

(NBP) is being proposed (Hamouchene & Aouat, 2014). This method is inspired by

Local Binary Pattern (LBP) method as NBP method are able to capture local

information and in the same time iris texture can be describe better.

2.2.1 Technical Implementation

There are few steps in iris recognition systems. The steps are image acquisition,

iris preprocessing, feature extraction and also the matching steps as shown in figure

below (Hamouchene & Aouat, 2014).

To obtain image from a person

To using sensor

Image Acquisition

To remove useless information from iris image

To extract the region of interest (iris)

To include segmentation (isolate iris ring) and normalization (provideinavariant iris area, form ROI into rectangular region)*

Preprocessing

There are two approaches:

Local Binary Pattern (LBP)

Neighborhood Binary Pattern (NBP)

Feature Extraction

distasnce measure between generated iris code and stored iris code are beingcalculated**

Matching step


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Note *Due to the two ring of the iris are not co-centric, Integro-differential

operator by Daugman is being used to detect the inner and outer boundaries

(Daugman, 2004).

**Daugman using Hamming distance and threshold around 0.34

Figure 7: Typical process of iris recognition

Figure 8: Conversion of iris image to iris code

LBP method which proposed by Ojala and Pietikainen is using analysis window

with the size of 3x3. This method is basically comparing each neighborhood pixels

with values of the central pixel and following conditions are given where if

neighborhood pixel value is above or equal to central pixel value, then it is encoded

with value of 1, otherwise the neighborhood pixel value is encoded with 0. After

being threshold by the central pixel values, a binary code can be obtained and this

binary code will be converted to decimal number.

Feature Extraction by using Local Binary Pattern (LBP)

The aim of LBP is to extract iris feature out from the normalized iris images.

The output from LBP is feature vectors with n x n dimension that serves as input for

LVQ classifier.


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Summary of LBP computation:

Figure 9: Computation of LBP

There is another feature that can be computed by using LBP method. It is known as C

contrast. C contrast is computed as the difference of average pixel of threshold equal

to 1 and the average pixel of threshold equal to 0.

Figure 10: C-contrast computation method

The pixel values in the LBP is summed and the central pixel is replaced with thisweighted sum value.

The weight pixels are matched with the threshold. If the threshold 1 is matched with theweight pixel, then the pixel will be the weight pixel. If the threshold 0 is matched with theweight, then 0 is displayed. The new set of data formed is the LBP data.

Every neighboring pixel is compared with the central pixel. If the neighbor pixel isgreater than the central pixel, then it is recorded as 1. If the neighbor pixel is less than thecentral pixel, then it is recorded as 0.

A sub-image with 3x3 matrix is formed.


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Feature Extraction by using Neighborhood Binary Pattern (NBP)

For NBP method, the neighborhood pixel values are being threshold as in LBP

but the difference is NBP method is comparing the neighborhood pixel values with

their respective next neighbor instead of central pixel values. The conditions are about

the same where a value of 1 is given if its gray value (neighborhood pixel value) is

greater than the next neighbor otherwise value of 0 is given.

Figure 11: Extraction of NBP pattern

By picking one the pixel value in the 3x3 analysis window (except for central

pixel value), the value is start comparing with adjacent values to determine 0 or 1.

The binary code is further converted to decimal value. If a small rotation happened to

the analysis window, different NBP code (binary code) will be obtained.

In order to prevent the rotation problem, an encoding process is being proposed.

This encoding process with pick the largest neighborhood pixel value and start

compare it with adjacent neighborhood pixel value. This encoding process can result

in the same binary code even though rotation on the analysis is happened.

Figure 12: Rotation invariant for NBP method.

A way to describe the NBP image is by using decomposing architecture. This

method will firstly divide the image into several blocks where mean value for each

block is being calculated and its variations will be encoded. Same as the conditionused in NBP, if value of one block is bigger than its neighbor block, a value of 1 will


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be given and 0 otherwise. Therefore, we will obtain a binary matrix of the variation

means and we can use it as the template of the iris texture.

Figure 13: Process of encoding mean variation

Intersection method is being used for the matching purpose by comparing the iris

images. Similarity distances between the two extracted matrices are being calculated

by performing below equation:

As show in the above equation, M1 and M2 are the variation binary codes for the

iris images. S value for the ith block is equal to 1 if the value of M1 for ith block is

equal to the value of M2 for ith block. Nb is representing the total number of blocks

and this value is based on the degree of decomposition of the iris image. If value of

Dis is above certain threshold, the two iris images (1 and 2) are referred as same

person.

Public iris database, CASIA is being used to evaluate the performance of the

system. As suggest by (Hamouchene & Aouat, 2014), three images from each person

are taken as reference and 80 images will be used as test images where each image are

referred as query. For each of the image, LBP histogram and mean variation of the

NBP image are being extracted. Between the querys feature and extracted features,

hamming distance is being calculated. By sorting the hamming distance from most

similar to less similar, the top three is being considered and the query iris is classified

by referring the majority (highest similarity).


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Figure 14: Recognition process flow

2.2.3 Result

Figure 15: Recognition rate for LBP and NBP method for each person

2.2.4 Discussion

From the above graph (figure 15), we can see that NBP method is way better than

the LBP method as the LBPs global rate is only58.75% where NBPs rate is 76.25%.

This is because of NBP method is comparing the neighborhood pixel values with its

adjacent pixel values instead of being threshold by the central pixel values. In other

words, we can say there is relationship among the neighborhood pixel values for NBP

method. This result had shown the robustness and efficiency of the NBP method as

compared with LBP method.

2.2.4 Conclusion

We can conclude that NBP method is having good performance as compared with

LBP. This is because of there is relative connection between the neighborhood pixels

as each one of them is being thresholded by the adjacent neighbor and encoded. Not


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only that, NBP image is being decomposed into number of blocks where their

variation of mean values are being extracted and encoded. This will result the binary

matrix being used as the feature descriptor for the iris image.

3.0

Overall Discussion

There are many approaches for the iris recognition. However, a classical iris

recognition system involved the series common steps which are the image acquisition,

iris preprocessing, feature extraction and matching step. For the preprocessing stage,

it involves the segmentation, filter and normalization.

From the above two methods for the iris recognition, they go through the common

series steps but each method is using different approach for the feature extraction and

matching step. The table below showed the summary for comparison between the

methods.

Table 3: The comparison between method 1 and method 2 for the iris recognition

Iris Recognition Method

Method 1

Combination of m and Hamming

Distance Approach

Method 2

Neighborhood-Binary Pattern (NBP)

Approach

Segmentation

Zig-Zag Collarette

(The irregular jagged line

between regions of pupillary zone

and ciliary zone in the surface of

iris )

Segmentation

Iris region

Feature Extraction

a. Haar wavelet decomposition

b. 1D Log Gabor wavelet.

Feature Extraction

a. Neighborhood Binary Pattern (NBP)


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Matching

a.

Support Vector Machine

b. Hamming Distance

Matching

a. Hamming Distance

Advantage

- The segmentation region is more

specific on the region of Zig-Zag

Collarette where it captures the

most important areas of iris

-

There are two classifiers which

increase the robustness of the iris

recognition.

-

Low false acceptance ratio

- High false rejection ratio

Disadvantage

- The data will be affected by the

rotation of the iris.

Advantage

- Rotational invariance

Disadvantage

-

With Hamming distance classifier

alone, the accuracy is not robust.

From the two methods above, they applied the pattern recognition approaches in

different way to identify the iris pattern. Feature extraction is considered as the most

importance stage for the pattern recognition because it extracts the relatively most

significant information from a given data, in this case is the iris pattern. Method 1

extracts the feature information through the wavelet. Wavelet is a useful tool with rich

mathematical contents and great applications. One of its applications is the image

analysis for pattern recognition. It is a function that is isolated with respect to time

and frequency, while the Fourier transform only respect to the frequency information.

In method 1, one of the wavelet transform used is the efficiently computable Haar

wavelet transform. The iris image is mapped from the space of pixel to that of Haar

wavelet feature that contains rich description of the pattern. On the other hand,

method 2 applies the Neighborhood-Binary Pattern (NBP) in the feature extraction.


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Generally, NBP is based on the Local Binary Pattern (LBP) which is an effective

texture operator which labels the pixel of an image by thresholding the neighborhood

of each pixel and considers the result as binary number. Thus, two different

approaches in the feature extract may result in different results for the recognition

rate, each approach with its own strength and weakness as shown in the table 3.

After the feature extraction, classifier is applied to classify or identify the iris pattern

for the identification. The method 1 uses extra one classifier known as Support Vector

Machine which is a supervised learning models work with an associated algorithm in

the pattern recognition. With the additional classifier, the error occurred is reduced

compare to method 2.

The results from both methods showed that method 1 is more stable and robust which

was around 99.91% of the average recognition rate, whereas LBP was 58.75% and

NBPs rate was 76.25%. However, it does not mean the method is better because

those methods are carried out in the different designed experiments.

4.0 Conclusion

In conclusion, the approach toward for iris recognition is different for both methods

and each approach with its own advantage and disadvantage respectively. However,

researches can be carried out for novel iris recognition with the combination of both

methods. Wavelet transform is used to decompose a given image to different kinds of

images and then processed with local difference between image pixel and its

neighborhood to build the LBP or NBP. The combination may increase the robustness

for the iris recognition.

5.0 Future Outlook

The future works involve are resolving challenges faced in developing an ultimately

high accuracy and reliable iris recognition system. Besides than solving the issues

caused by special iris condition as mentioned in introduction, noise that caused by

environments factors such as unfavourable lighting, large stand-off distances and

moving objects (Ross, 2010). These conditions can caused nonideal irides images to


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be captured and increase the difficulties in processing. Robust image restoration

schemes are needed to enhance the quality of such iris images. In the intervening

time, researchers might develop a robust ocular multi-biometrics system by

combining the iris with facial biometrics. A more accurate and improved matching

identification can be done compare to only using low resolution iris image.

Apart from that, the hardware architecture of the system has to be improved to

complement with the increasingly complex algorithm which is used to enhance the

reliability and functionality of current used solutions (Grabowski & Andrzej, 2011).

The uses of iris identification system in large scale has raised the concerns about the

iris template security and the retention of owners privacy. A centralised databased

which stored millions of iris template and data must be secure with extra security to

prevent thief and also backup system to avoid data lost. This security system needed

to be updated and monitor frequently as the digital thief will also improves their

techniques from time to time.

6.0 Reference

Ali, H., & Salami, M. J. (2008). Iris recognition system by using support vector

machines. In International Conference on computer and communication

engineering(pp. 516-521).

Burge, M. J., & Bowyer, K. W. (2013).Handbook of iris recognition. Springer.

CASIA iris image database (v1.0), The National Laboratory of Pattern Recognition

(NLPR), Institute of Automation, Chinese Academy of Sciences (CAS), 2006

Chen, C., & Chu, C, (2005). High performance iris recognition based on 1-D circular

feature extraction and PSO-PNN classifier. Expert Systems with Applications,

36(7), 10351-10356.

Daugman, J. G. (1993). High confidence visual recognition of persons by a test of

statistical independence.Pattern Analysis and Machine Intelligence, IEEE

Transactions on, 15(11), 1148-1161.


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Daugman, J. (2004). How iris recognition works. Circuits and Systems for Video

Technology, IEEE Transactions on, 14(1), 21-30.

Grabowski, K., & Napieralski, A. (2011). Hardware architecture optimized for iris

recognition. Circuits and Systems for Video Technology, IEEE Transactions

on, 21(9), 1293-1303.

Hamouchene, I., & Aouat, S. (2014). A New Texture Analysis Approach for Iris

Recognition.AASRI Procedia, 9, 2-7.

Masek, L. (2003).Recognition of human iris patterns for biometric

identification(Doctoral dissertation, Masters thesis, University of Western

Australia).

Patil, C. M., & Patilkulkarani, S. (2009, October). An approach of iris feature

extraction for personal identification. InAdvances in Recent Technologies in

Communication and Computing, 2009. ARTCom'09. International Conference

on(pp. 796-799). IEEE.

Rai, H., & Yadav, A. (2014). Iris recognition using combined support vector machine

and Hamming distance approach.Expert Systems with Applications,41(2),

588-593.

Rashad, M. Z., Shams, M. Y., Nomir, O., & El-Awady, R. M. (2011). Iris recognition

based on LBP and combined LVQ classifier.International Journal of

Computer Science & Information Technology (IJCSIT) Vol, 3.

Ross, A. (2010). Iris recognition: The path forward. Computer, 43(2), 30-35.

Westmoreland, B. F., Lemp, M. A., & Snell, R. S. (1998). Clinical Anatomy of the

Eye.Oxford: Blackwell Science Inc.

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Assignment Pattern v2