image and video coding - 14024050007 - final draft (reviewed) 3.0

Iris Recognition February, 2015

Iris Recognition February 2015

IRIS RECOGNITION

Muhammad Usman

University of Management and Technology (UMT)

Lahore, Pakistan

E-mail: [email protected]

Abstract

Iris Recognition has gauged much attention for over a past few decades. No doubt, it is one of the most

accurate domains as far as security is concerned. People had worked a lot in this area. It deals with all, starting

from acquiring image from hardware devices to matching iris images using some mathematical, statistical,

probabilistic or intelligent models. This article evaluates and compares among few of the well-known state of

art iris recognition models.

1. Introduction

Authentication and authorization is the key

element in any secure of supervised environment.

The domain of Iris recognition has been gauging

attention for over a past few decades. With

advancements in iris matching algorithms, the

broader applications and new technology is

witnessing the demand of related tools and

applications. These systems are based on the basic

requirement of user to achieve authentication/

authorization in a trusted and accurate manner.

Previously, identity cards, passwords or some other

document used for this purpose. Such a procedure

was based on two queries. First is “Who you are? And the Second is “What you have?” This type of information creates dependency in Authentication

and authorization. They might expect a card, that

must be carried permanently thus security is weak

and prone to failure. To counter this problem,

biological features are used to perform authorization

and authentication. Various techniques and models

have been used to get feature extracted, as in this era

it is easy to perform such computations on a small

cheaply available device. Scientists have used

various biologicals features of a person for this

purpose such as gait, iris, thumbprints, speech, facial

features, vein structure, ear pattern, fingerprints,

sutures and etc. Biometric methods typically perform

numerous steps to yield an outline and generate a

specific code that contains peculiar information about

the biological features in a numeric and succinct

form. Ultimately a vector of such features enables a

system to uniquely recognize each user with the help

of a classifier.

As discussed earlier, Biometrics based on physical

or behavior characteristics are used to uniquely

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identify an individual. With the change of

individual’s requirement, iris recognition has been

considered the most accurate among most of

authorization and authentication techniques. Human

iris has an unusual construction and provides rich

texture information. This textural information

embedded in the iris uniquel for each human and is

inimitable over time, hence it is preferred choice as

tool for identification of an individual. Dissimilarities

that exist in the biological features seem to alter over

time due to factors like growth and aging. As

compared with other biometric features iris is the

most stable and reliable for identification.

Identity evasion is a serious concern and it’s a tremendous challenge to robust fool proof

identification system. Use of iris for identification

provides a great dividend since iris is unique, remains

even and secure throughout life period. Speed,

simplicity and precision are main benefits of iris

recognition system. It is an efficient technique and

conferring to conditions its error rate is also less.

2. RELATED WORK:

2.1 LMD (Local Mean Decomposition)

Iris can be recognized using LMD. It is totally a data-

driven method and it does not have any permanent

filter. Image preprocessing extracts only the iris features from the entire eye image. Wei-Yu Han [8]

worked on this domain. There are three parts in

which this extraction can take place. This technique

can be used to observe many data types. It can decay

a signal into different parts which we say product

functions. Tieniu Tan suggested [5] almost the same

algorithm with another technique. His technique is

based on textures of iris and even and obvious

lightning. In this paper, Algorithm used was “Multi –

modal fusion”, in which it takes the entire eye as a biometric pattern that contains the information of iris.

Moreover, structure of eye region also returns

important data that can be used as secondary

personas to match noisy iris image. Framework for

such environment is well anticipated here.

2.2 Fuzzy Integration

Fuzzy integration can also be helpful to recognize

iris. Fuzzy logic [2] opens a gate for a system to the

reason for the uncertainty. It is a helpful tool for

modeling composite machines. Though, it is time and

again not easy for developers to define the sets and

rules used by systems. In that account, a genetic

algorithm provides us with a solution that finds not

only the architecture but returns a fair suitable

recognition rate. In one of research papers, Patricia

Melin stated in [2]that, once it is confirmed that the

genetic algorithm can produce a sound optimization

result, in which the algorithm runs ten times to find

Standard Deviation and the number of layers.

Summary of this exercise with the results of the 10

experiments on the basis of comparisons with

different optimization methods is presented in her

work. Feature Extraction Methods for Iris

Recognition is a useful technique to get iris

recognized. It gets the image, recognizes the by

separating the iris portion from the entire eye, which

is referred as segmentation. In third step segmented

iris is normalized. Then available samples in database

are matched with the iris. Hamming distance can be

used to calculate the distance between two iris

templates.

2.3 Ridgelts Transformation

Another useful technique is to apply Ridgelet

transform on Iris. Ridgelet transforms [6] are the

mixture of Radon transforms and Wavelet

transforms. They are appropriate for extracting the

plentifully present textural data that is in an iris. The

method anticipated here uses the ridgelets to form an

iris mark and to symbolize the iris. Author

contributes towards creating an enhanced iris



recognition system. Experimental has shown

promising results that states the precision of 99.82%.

2.4 DRS (Daugman’s Rubber Sheet)

Mr. P.P.Chitte suggested that Daugman’s Rubber Sheet [11] Model is a healthy technique for

respective domain. In his paper, he suggested iris

blend technique based on (ICA), (PCA), and

Daugman’s rubber sheet. Algorithm has confirmed to be progressively more precise and dependable.

Author has compared results obtained from all three

algorithms and has selected the best technique for

respective area. Real- time Iris Segmentation is a

technique used by “Juan Alejandra” which maps the iris texture in Daugman’s doubly coordinates. Author has also applied an adaptive Hough transform to

calculate the estimated centre of the iris. Based on the

result of the first, polar transform finds the first papillary boundary whereas anellipsopolar transform

finds the second. Here both images for visible wavelength can be processed in an identical conduct.

2.5 Geometric Key Based Algorithms

Iris Recognition using stabilized iris Encoding and

using geometric key based Iris Encoding are purely

decision based algorithms. A nonlinear approach for

concurrently explanation for local uniformity of iris

bit and the overall quality of the weight is used in

these algorithms. The technique efficiently penalizes

the fragile bits while all together it satisfies consistent

bits. Through Experiments the proposed approach

can root major improvement in iris matching

accuracy. Iris can also be identified by using neural

networks. In this method after getting and processing

the image, distance of iris between left to right and

from top to bottom is calculated. At last, they used

neural network for to train and test the algorithm. The

best accuracy is 97.5%. Iris Recognition based on

Local Mean Decomposition is also a good technique

for matching and recognition. It is done in four

simple stages: a) Quality assessment of Image, b)

Preprocessing the image, c) Extracting the features

out, d) Matching image with database. To assess the

performance of this approach, several similarity

measures are used to view the results based on

experiments using both the CASIA and ICE iris

image databases.

3. TECHNICAL FRAMEWORK

A basic Iris recognition building consists of three

pillars. After getting the image, first of all Iris region

is segmented, secondly some transformation is

applied to get the features extracted and lastly the

image is matched/ classified through some database.

The process is well explained in the figure below.

Iris Recognition

Iris SegmentationTraining &

Classification

Transformation &

Feature Extraction

Mathematical

Model(Alcohol

Assumption)

NIR(Image Near-

Infrared)

Fuzzy Integration

Corner Detection

Binary Segmentation

Method

Iris bits Stabilization

Encoding

Geometric Key based

Iris Encoding

LMD (Local Mean

Decomposition)

Multi-Model Fusion

Fuzzy Integration

Simple Mean

calculations

Ridgelts

Gabor Filter Bank

Daugman’s Rubber Sheet Model

Geometric key based

Iris Encoding

Neural Network

algorithm

Decision based

algorithm

Zernike Moments

Three level layered

architecture

Zernike Moments

Neural Network

algorithm

Decision based

algorithm

Figure 1: Iris Recognition

3.1 Alcohol consumption based



Author named RichaSingh [1,2] described usage of

alcohol results in the dilatation/constricting the pupil

which causes change in texture ending with affecting

the recognition performance. If � � Is the image of

user “U” taken before alcohol consumption and �

is iris image after alcohol consumption. Let �� and �� be the distance

between the major and minor axis of pupil and �� and �� the distance

between major and minor axis of iris boundaries. The

relation below defines the area.

�� = � × �� × ��

�� = � × �� × ��

Likewise, the area of the iris boundary can be

calculated by,

� � � = � × � � � × � � �

� � = � × � � × � �

Experiments performed to test alcoholic usage are

given below.

1. Matches pre alcohol iris images.

2. Matches post alcohol iris images.

3. Matches pre and post alcohol iris images.

The first two were done for achieving precision,

where as the third experiment is performed to test the

intake of alcohol. This technique can be used for

cooperative systems as well as non-cooperative ones.

First of all, the original images are preprocessed.

Then, ordinal actions and color histogram in the

image are identified to distinguish iris data, textual

representation and semantic label plays their part to

get eye patterns. Then, 4 matching scores are

obtained by different algorithms. Finally, a union

strategy is applied to make the final difference score.

They used 'IIITD Iris under alcohol influence'

database for comparison of different datasets. This

database consists of 220 images of iris that were

taken before alcohol consumption and 220 images

after alcohol consumption from different 55 subjects.

Subjects then consists of different age groups. This

database shows 95% accuracy rate.

3.2 Fuzzy Integration

Other technique named as fuzzy integration plays a

vital role to recognize and individual by Iris. Modular

neural uses iris image and it can be trained by a

model. Author uses genetic algorithms with multiple

techniques, like: gating network or fuzzy integration.

The Image preprocessing is done by applying

comparison wavelet 2D technique. Furthermore

Image is also enhanced by leaving the iris behind.

Figure 2: Iris Enhancement

Once preprocessing is done, the image is compressed

in to matrix that uses wavelet transform with some



levels of decomposition.

Figure 3: Image Compression

Once this process is done, fuzzy integration is

applied on it.

They have used CASIA database for comparison

process. They took three modules which consists

different number of images. And then run the

algorithm ten times on each of the module to get the

best accuracy rate and high efficiency. They

concluded that using three layered neural networks

for iris recognition shows the best recognition rate of

99.37%.

3.3 Haar Wavelet Transform

Dolly Choudhary and Ajay Kumar Singh suggested

another feature extraction method for iris recognition.

They presented it after a long study on feature

extraction algorithms. Authors have discussed

feature extraction methods in which they have used

feature encoding along with corner detection. They

described Haar wavelet transform that is supported

with programmed Gabor filter Bank with some

statistical pattern based on recognition model.

Following are the steps that are involved in the whole

process.

a. Corner Detection Based Iris Encoding

Author captured a very good quality image and then

the texture of iris is stored. Estimated distance

between user and source of light is taken as 12 cm.

Now localization step involved two parts which are

calculation of vertical and horizontal distances such

as:

�� = {− − − ; ; }

� = {− ; ; − }

b. Feature extraction using Haar wavelet

Here, G vertical is obtained by convolving image

with C vertical and G horizontal is obtained by

convolving of image with C horizontal. � = �� + � �

c. Comparison

Results are compared by using different

mathematical models. The image obtained is finally

Image Acquisition

Segmentation

Normalization

Feature Extraction

Matching



divided in five levels based according to iris texture.

Figure 5: Image Division

Above is an iris image up to five level patterns cD1,

cD2, cD3 and cD4 are almost same so one of them

may be selected to decrease redundancy. Since cD4h

is identical to the previous one and it is the smallest

in size, it can be taken as a diplomat of all the

information the four levels carry. The last level does

not hold the same textures and it should be chosen as

a whole.

Authors used CASIA database for the comparison of

images. They used accuracy rate of 95.4% by using

two systems of FRR and FAR. High accuracy rate

was achieved by using three training set of iris

images. Authors took 60 images from different

subjects and then classified them in 10 different

groups for better recognition rate as well as for

achievement of high performance.

3.4 Daugman’s Rubber Sheet

Daugman’s Rubber Sheet is also one of a healthy technique for feature extraction. It is carried out

through variations characterized by the look and loss

of a significant image. A spatial filter bank is used

for feature extraction and matching shown below.

Figure 6: Rubber Sheet Algorithm

The aim behind the process is to obtain a code for iris

recognition. The process starts with an outline of iris

by explaining feature extraction. Image is processed

in two steps. First step is Graying and second is edge

detection. Then, a certain threshold is applied to the

resultant image. Furthermore, image is normalized by

applying Daugman’s rubber sheet model. The above

image shows the steps applied.

Authors used UBIRIS database for concluding their

results. They took 230 dataset of 5 images per

dataset. These 5 images were of same person and

hence they used data of 230 different people of

different ages. Accuracy rate was 98.79%.

3.5 Ridgelets

LeninaBirgale and ManeshKokare suggested that

ridgelets can also be used for feature extraction.

Mask can be designed by using following algorithm:

Iris Authentication

Image Processing

Graying

Segmentation

Edge Detection

Thersholding

Circular Hough Transform

Normalization

PCA?ICA?Daugman

Thersholding

Bit Pattern

Pattern Matching Encryption/

Decryption of Data



Iris Database Test Iris




Matching

Matching Result

Figure 7: Algorithm

� � = ∑ ∑ � cos �� + � , � sin ��=

59= + ��

Where: � � = Area of the iris and the pupil. � � = Maximum radius of the iris. � , ��= Coordinates of the centre of the pupil � = Initial radius of the iris.

The wavelets relay on the scales of the point position

and ridgelets relate the scales of the line positions.

So, wavelets are very superior at representing point

singularities. But, when there is need to represent

singularities, wavelets fails and ridgelets predict the

better solution. The feature vector size used is 1X4.

It also skips normalization and uses the ridgelet

transform for feature extraction. To take out the

signature of the iris they have calculated the first

level energy by using the following equation,

�� = = ∑ ∑ | � , |−=

−=

� , = Discrete function

Energy of above relation can be calculated by,

� = � ∑ ∑ , − �==

, = cth

wavelet decomposed sub-band. � = Size of wavelet decomposed sub-band. � = Mean value of cth

decomposed sub-band.

Euclidean distance metric is exercised to calculate to

equalize the value for the known pair of images in

database. “Zero distance” involves an ideal

counterpart and the image tells a difference as the

distance increases.

,� = √∑ − =

= ath

element of ith

database iris signature.

= ath

element of the jth

query iris signature vector.

Authors used three databases which are CASIA v1,

CASIA v3 and UPOL. They took experiments with

756 images by using CASIA v1, 660 images by using

CASIA v3 and 384 images by using UPOL database.

3.6 Hough and Ellipsopolar Transforms

Hough and Ellipsopolar Transforms used various

statistical models in process of iris recognition.

Proposed algorithm is binary segmentation method.

The first part finds a center O of an image by an ovel Hough transform, through tells us about concentric

rings. It then extracts a polar symbol by using O as



origin. Here every border line edge has similar course,

which reduce the rate of edge detection.

An initial boundary Bi is noticed which takes input

by modeling a circle in Cartesian coordinate’s

gradient for each angle. It then smoothes the curve

obtained and ends with remapping to Cartesian

coordinates. Finally it fixes corner values by an

ellipse. This method is based on two hypotheses.

H0 ( B = P) , H1(B=L)

It maps concentric ovals along boundary“B”.AsP and L ideally should posses same center, so algorithm

should go with the normal values. Depending on the

result, e(energy) of one of the hypothesis is

discarded. If

e (P0) > e(L0) then H0 is rejected

ifP = P0, or L = L0.

It is now ready for rubber sheet model to be applied.

Authors used databases of Casia V3, Ubris.2, ICE,

FRGV and FERET for images comparison. They

took three different datasets for three different types

of databases. First dataset includes 1332 images,

second dataset includes 1000 images while third

dataset consists of 420 images. They have used

multiple algorithms for segmentation of iris on

datasets of images and after comparison of all

algorithms, they calculated that average accuracy rate

was recorded as 95% and minimum computation

time was 0.68s. They also determined the usability

rate of all algorithms and average usability rate was

92%.

3.7 Zernike Moments

Iris recognition using “Zernike Moments” is one of the fine decision based algorithms.

The iris segmentation begins with the image

enhancement. It uses retinex algorithm in which a

low pass filter is used that contains high frequency in

the enhanced image. The detected noisy pixels are

then packed with a binary mask to remove out all

such noisy pixels. The last step of iris segmentation is

estimation of noise such as eyelashes, shadow and

eyelids.

Authors concluded their results by using three

different databases such as UBIRIS.v2, FRGC,

CASIA v4-distance. They took two kind of images.

One which are visible in normal light and one which

are only visible using IR light. They took different

datasets for different database. 1015 visible images

were taken for UBIRIS.v2. 1234 visible images were

used for FRGC database. And 1016 IR images were

compared using CASIA v4 database. They concluded

UBIRIS.v2 database as best results shown. Accuracy

rate was high and performance was more than 90%.

CASIA was less usable because of high cost.

3.8 Decision Based-Occlusion Estimation

Image

Enhancement

Reflection

Detection

Coarse

Segmentation

Boundary

Refinement

Normalized Iris

image (Occlusion

Mask)

Occlusion

Estimation

Input Image

Iris & Pupil

LocalizationSegmented Iris

Normalization

Figure 9: Decision Based –Occlusion Estimation

In this technique, the decision is based on outputs of

two steps. First step is named as Iris bits stabilization

encoding. In which the obtained image is normalized

and then further preprocessed and mapped by using

nonlinear weighting strategy. Meanwhile image is

matched using a trained probability map which leads

through to the non linear weight estimation. Once the

features are extracted they are passed through a

weighted feature that ends in storing the image in

database. Second step is Zernike Moments Phase

Preprocessing &

MatchingFeature Extrraction Weighted Feature

Non – Linear

Weight Estimation Matching

Block Division &

Vectorization

Feature

Extraction(Zernike’s Moments)

Non- Linear Weight

Estimation

Zernike Moments

Phase Fratures

Matching

Scor

e1

Scor

e2

Final

ScoreDecision

Occlusion Mask

Normalized Iris

Image

Figure 8: Zernike Moments [1]



Features. This section is further categorized in two

different parts. First the Normalized iris image is

vectorized and features are extracted through Zernike

Moments. Meanwhile, Occlusion mask obtained is

passed through under non – liner weight estimation.

Zernike Moments Phase features takes input as the

features extracted from Zernike moments and it

generates an output image that is used for cross –

phase matching. Finally images obtained from

algorithms are matched and scores are combined and

a trained model is used to take the decision.

They used three main databases for comparison such

as UBIRIS v.2, FRGC, CASIA.v4-distance. They

used three algorithms with different datasets and they

concluded that error rate was almost same while

performance was improved for algorithms.



3.9 Gabor filter

Feature ExtractionLocal Mean

EstimationGeoKey Encoding

Geometric Key

Matching

Binary Features Matching Scor

e1Final

ScoreDecision

Phase

Quantization

Normalized Iris

Image

GeoKey

Transformation

Scor

e1

Figure 10: Gabor Filter Algorithm

Another simple approach for Iris Recognition is

using Geometric Key based Iris Encoding. In which

the normalized image is passed through a Log –

Gabor Filter.

Iris images can be recognized by decision based

algorithm. Image samples are collected and image is

then processed. Furthermore, the distance between

iris from left to right and top to bottom is calculated.

Biological neural network processing consists of

three steps. First the image is captured, and then the

respective image is processed by imagej tool for

feature extraction. Author suggested neural network

for classification. Feed forward back propagation

neural network are also produced here. A number of

neural networks are created for different group of

dataset, and finally the performance of each created

networks is measured.

Finally neural network is used for training and testing

purpose. Respective training algorithm and setting

multiple parameters for training and CASIA iris

database are used in this effort. The best accuracy

shown is 97.5%.

3.10 Neural Network Algorithm

Database

Image Processing

Feature Extraction

Length

Left to right

Breadth

Top to bottom

Dataset creation

Neural Network

model

Result

Figure 11: Neural Network Algorithm Flow

Figure 12: IR through Neural Network

It is one of the typically used algorithms. It has three

layers

1. Input layer

2. Output layer

3. Hidden layer

First two layers have some in between layers(hidden

layers), which helps in performing needed

calculations. First is the number of hidden layer and

second is the number neuron in each layer. Based on

these different training algorithm was designed.

Authors used three most frequently used databases

such as UBIRIS.v2, FRGC and CASIA-v4 distance.



They took 1000 images for UBIRIS.v2 from 171

different subjects, 1085 images for FRGC from 149

different subjects and 935 images for CASIA from

131 different subjects.

4. Comparative Analysis

In the respective research, few methods are proposed

to recognize human iris. Though, every method has

pros and cons yet they are quite effective in terms of

accuracy and efficiency. Methods can be mapped

graphically as more efficient or more accurate. For an

overview, techniques discussed are listed below.

1. LMD (Local Mean Decomposition)

2. Multi-Model Fusion

3. Fuzzy Integration

4. Simple Mean calculations for feature

extraction

5. Ridgelets

6. Gabor Filter Bank

7. Daugman’s Rubber Sheet Model 8. Geometric key based Iris Encoding

9. Neural Network algorithm

10. Decision based algorithm

11. Iris recognition based on Zernike Moments

12. Three level layered architecture

Here some of the methods are decision based, few are

good in matching and few used receiver operating

characteristics to recognize human iris. Some of them

are based on retrieval values and a little of them uses

some classifier. To represent the accuracy and

efficiency of each algorithm, results from the each

respective algorithm are used as a source with the

same database.

On ROC curve, LMD, Fuzzy Integration and Neural

network algorithms showed healthy results. Since,

time complexity depends on the how complex the

mathematical operation used in the algorithm. Neural

Networks consists of three layers and for every layer

it performs calculations. So when it comes to time

complexity, it is not one of the suggested models.

Moreover, it uses more databases for training

purposes for all three layers.

Iris recognition based on Zernike Moments is a

purely precision recall technique in which the

algorithm splits in two different algorithms and at the

end scores are matched based on the decision.

Precision is how much selected items are relevant

whereas recall means how many relevant items are

selected. The algorithm achieves precision by

performing intersection between relevant and

received results. Mode of their result is then dividing

them with ones it retrieves. In recall, mode of the

result is divided by the relevant ones. The proposed

method has shown good precision and efficiency in

iris recognition.

LMD, Multi-Model Fusion and Simple Mean

calculation for feature extraction have the same time

complexity. Ridgelets, Rubber Sheet Model and

Geometric key based algorithm has shown promising

results in accuracy and space complexity. They use

less space and can produce an accurate iris match by

using CASIA – I database.

For comparisons, results produced in their algorithms

are used using recommended databases. If they are

mapped altogether, in terms of efficiency and

accuracy, they can be compared easily. Here neural

network is the best example of trade off between

accuracy and efficiency. Algorithms based on neural

networks showed good precision but when it comes

to efficiency (time complexity), they showed

ordinary performance.



Figure 13: False Positives

Figure 14: True Positives

5. Conclusion

A fine comparison of above mentioned algorithms

for true positives and false positives is made.

Moreover, percentages for True Positives, False

Positives, efficiency and accuracy can also be

mapped altogether with the help of a bar graph.

Though every problem has its own area of concern

but to get the iris recognized, every algorithm has

used kind of same steps followed by different

mathematical models. Neural Networks used layered

architecture and Zernike Moments had used decision

based approach in which they calculate total of

collected scores and then decide for the sample to be

selected. Fuzzy integration is a quick way to get iris

recognized but it is not good when it comes to

address the issues like proper light or noise removal.

Our suggested approach is, if neural networks use the

mean value for calculating the layered architecture

False

Positives %

LMD (Local Mean

Decomposition)Multi-Model Fusion

Fuzzy Integration

Simple Mean calculations for

feature extractionRidgelts

Gabor Filter Bank

Daug a ’s Rubber Sheet Model Geometric key based Iris

EncodingNeural Network algorithm

Decision based algorithm

Iris recognition based on

Zernike MomentsThree level layered

architecture

True

Positives %

LMD (Local Mean

Decomposition)

Multi-Model Fusion

Fuzzy Integration

Simple Mean calculations for

feature extraction

Ridgelts

Gabor Filter Bank

Daug a ’s Rubber Sheet Model

Geometric key based Iris

Encoding

Neural Network algorithm

Decision based algorithm

Iris recognition based on

Zernike Moments

Three level layered

architecture



approach, it can result with good efficiency and accuracy.

ROC Curves analysis

x = false positive rates (1-specificity)

y = true positive rates (sensitivity)

Fitt

ed

cur

ve:

y =

0.4

Ln(

x)

+

0.9

1

R^

2 =

0.8

985

Are

a

und

er

cur

ve

=

0.5

19

Est

imated ROC curve with

Column 1 = false positive rates (1-specificity)

Column 2 = true positive rates (sensitivity)

LMD

(Local

Mean

Decom

positio

n)

Multi-

Model

Fusion

Fuzzy

Integra

tion

Simple

Mean

calcula

tions

for

featur

e

extract

ion

Ridgelt

s

Gabor

Filter

Bank

Daugm

a ’s Rubber

Sheet

Model

Geome

tric key

based

Iris

Encodi

ng

Neural

Netwo

rk

algorit

hm

Decisio

n

based

algorit

hm

Iris

recogn

ition

based

on

Zernik

e

Mome

nts

Three

level

layere

d

archite

cture

False

Positives %11 10 15 22 32 15 20 13 12 20 14 15

True

Positives %89 90 85 78 68 85 80 87 88 80 86 85

Accuracy % 96.3 86.4 90.78 80.86 88.6 94.5 91.6 94 98.5 89.5 88.7 78.4

Effiency % 78 89.6 86.7 77.8 95.7 90.6 89 77 85.5 78 84.6 89.5

0

20

40

60

80

100

120

Pe

rce

nta

ge

s

Overall Balance



Figure 15: ROC Curves

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Straight Line

Wei-Yu Han

Sunpreet S. Arora, Mayank Vatsa,

Richa Singh

Patricia Melin, Victor Herrera

Dolly Choudhary

Lenina Birgale and Manesh Kokare

R.G.P.V. Bhopal, M.P

Mr. P.P.Chitte, Prof. J.G.Rana

Chun-Wei Tan, Ajay Kumar

Sunpreet S. Arora

Andreas Uhl and Peter Wild

Chun-Wei Tan, Ajay Kumar 2

Gajendra Singh Chandel, Ankesh

Bhargava2



6. REFERENCES

1. Sunpreet S. Arora, MayankVatsa, Richa

Singh IIIT Delhi New Delhi, India Iris

Recognition (National Science And

Technology Council)

2. Genetic Optimization of Neural Networks

for Person Recognition Based on the Iris

3. Gajendra Singh Chandel1, Ankesh

Bhargava2 Sri Satya Sai institute of

science and technology, R.G.P.V. Bhopal,

M.P.

4. Iris Recognition based on Local Mean

Decomposition Wei-Yu Han1, Wei-Kuei

Chen1, ∗, Yen-Po Lee1, Kuang-shyr Wu1

and Jen-Chun Lee2 ChienHsin University,

Taoyuan, Taiwan 2 Department of

Electrical Engineering, Chinese Naval

Academy, Kaohsiung, Taiwan Received:

3 May. 2013, Revised: 28 Aug. 2013,

Accepted: 29 Aug. 2013 Published online:

1 Apr. 2014

5. Noisy iris image matching by using

multiple cues Tieniu Tan ⇑, Xiaobo

Zhang, Zhenan Sun, Hui Zhang National

Laboratory of Pattern Recognition,

Institute of Automation, Chinese

Academy of Sciences, P.O. Box 2728,

Beijing 100190, PR China.

6. Iris Recognition Using

RidgeletsLeninaBirgale* and

ManeshKokare**

7. Analysis of Template Aging in Iris

Biometrics Samuel P. Fenker and Kevin

W. Bowyer Department of Computer

Science and Engineering Univ. of Notre

Dame, Notre Dame IN 46556

8. IEEE Trans. Information Forensics and

Security, 2014 Efficient and Accurate at-

a-distance Iris Recognition Using

Geometric Key based Iris Encoding

Chun-Wei Tan, Ajay Kumar

9. IEEE Trans. Image Processing, 2014

Accurate Iris Recognition at a Distance

Using Stabilized Iris Encoding and

Zernike Moments Phase Features

10. Weighted Adaptive Hough and

Ellipsopolar Transforms for Real-time Iris

Segmentation Andreas Uhl and Peter

Wild∗ Multimedia Signal Processing and

Security Lab Department of Computer

Sciences, University of Salzburg, Austria

11. Multi-stage Visible Wavelength and Near

Infrared Iris Segmentation Framework⋆

Andreas Uhl and Peter Wild

12. Evaluation of the effects of Gabor filter parameters on texture

classificationFrancesco Bianconia, Antonio

FernándezbaDipartimentoIngegneriaIndu

striale, UniversitàdegliStudi di Perugia

Via G. Duranti 63, 06125 Perugia (Italy)

bDepartamento de DisenõenIngenier´ıa, Universidad de Vigo E.T.S.I.I. - Campus

Universitario, 36310 Vigo (Spain)

image and video coding - 14024050007 - final draft (reviewed) 3.0

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