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American Journal of  
Geospatial Technology (AJGT)

Enhancing Atm Security System by Using Iris (Eye) Recognition
Irum Ashraf1*

Volume 3 Issue 1, Year 2024
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v3i1.2967
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: May 10, 2024

Accepted: June 12, 2024

Published: June 15, 2024

Newly invented Iris recognition which is a part of  biometric identification,offering and 
purposing an antique method for personal identification, authentication and security by 
analyzing the random pattern of  the iris. By using iris recognition system recognizes the 
identification of  a person from a captured image by comparing it to the human iris patterns 
stored in an iris template database. The iris template database has been carried out by  using 
three steps the first step is segmentation. Hough transform is used to segment the iris region 
from the eye image of  the CASIA database. The noise and blurring  due to eyelid occlu-
sions, reflections is eliminated in the segmentation stage. The third step is  normalization. A 
technique based on Hough Transform was employed on the iris for creating a dimensionally 
steady and compatible representation of  the iris region. The last step and fourth step  is fea-
ture extraction. In this Local Binary Pattern and Gray level Cooccurrence Matrix are used to 
extract the features. At last template of  the new eye image captured  will be compared with 
the iris template database using Probabilistic Neural Network.

Keywords
Image Preprocessing, Segmentation, 
Normalization, Feature 
Extraction

1 Grand Asian University, Sialkot, Pakistan 
* Corresponding author’s e-mail: irum.ashraf@gaus.edu.pk

INTRODUCTION
About 25,000 accounts are opened on the daily according 
to the RBI report around the world. In the past transaction 
cannot be done in emergency situation due to crowd 
and it also takes more time so John Shepherd invented 
ATM (Automated Teller Machine). Due to increase in 
the user account made to change the banking system. 
As users and customers increased drastically , fraudulent 
act also increased equally so banking system has given 
more importance to security system which is prior of  the 
customers and users requirement. Hence the requirement 
for banking security is increased with the passage of  time. 
Biometric system measures the antique specifications of  
a person so that no one can break the system. Because of  
this  feature of  Biometric, this idea evolved in banking 
systems and sectors. Biometric includes Iris recognition, 
sound recognition, face features recognition and 
fingerprint/thumb recognition. Among all those types 
of  Biometrics used, Iris recognition transaction through 
ATM card it the easiest  and simple, all of  the customers 
and users are depend on card system. But at the same 
time ATM cards pin or password can be easily traced 
and account can be accessed. It is important to keep the 
record  transaction reports made by use. To overcome 
these defaults. card less system is  should be created by 
using Red-tacton. Red-tacton uses the surface of  the 
human body as a safe, high speed network transmission 
path. As per newly invented iris system recognition, It 
also provides a complete and reliable security system to 
the users as  it allows access to only those individuals 
whose iris is matched with the database and deny access 
to all others very reliably. This system has four stages: 
First is the Image Acquisition. In this image is taken  with 

proper illumination, distance and other specifications 
affecting image quality and its other features. This step 
is important because image quality plays an important 
role in iris Localization step.Another Second is Image 
Segmentation in which iris is recognition. In this step 
of  feature extraction stage, antique specifications from 
the segmented iris has been extracted to create an iris 
performa or template.Moreover this template is used for 
Iris recognition.However fourth one is matching with 
an original image taken and saved in database. On this 
stage it is to be verified that the iris matches image saved 
in data based or if  it doesn’t matches then its rejected. 
Iris recognition is an automatic method of  biometric 
identification that uses an antique specification of  evey 
single user.Iris an internal organ of  our body that is 
visible from outside whose patterns are complex random 
patterns which are most unique and stable.
Howeve overall biometric technologies used for human 
authentication is one of  the most authentic and accurate 
for securing banking and ATM system. Iris  recognition 
is one of  the stable and reliable out of  other biometric 
techniques such as face recognition, finger recognition, 
hand and finger geometry, just because of  its special and 
non- invasiveness of  the iris pattern. The iris region, the 
part between the pupil and the white sclera provides 
many minute visible characteristics such as freckles, 
cornea stripes ,furrows, crypts which are unique for 
each individual(Daugman, 1993). If  it comes to the eye 
matching, both of  our eyes doesn’t match with each 
other. However the chance of  matching two people 
with same specification is almost zero  which  makes the 
system more frequent and stable  when it comes to the 
security matter.



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Am. J. Geo Spat. Technol. 3(1) 69-75, 2024

Figure 2: Iris Recognition

Figure 1: Biometric Technologies

LITERATURE REVIEW
Patented algorithm uses Iris recognition system which 
was developed by John Daugman BY using integro 
differential operator in his algorithm to find inner and 
outer boundaries of  Iris, including the detection of  
upper and lower eyelid boundaries.For  normalization 
Daughman’s rubber sheet model is used  where in the 
circular Iris region is unwrapped into rectangular block 
of  fixed dimension. Feature extraction is performed 
using 2- D Gabor and hamming distance later on which 
is  used for code matching. 1 in 4 million is the theoretical 
false match probability in this method. Yang Hu et al(Hu, 
Sirlantzis, & Howells, 2016) has suggested a method 
for optimal generation of  iris codes for iris recognition. 
This suggested method has been verified later  that the 
traditional iris code is the solution of  an optimization 
problem, where the distance between the feature values 
and the iris codes has been reduced. This method also 
proves that more frequent  iris codes can be obtained 
for the optimization problem by adding somevintial  
terms to the objective function. The two additional 
objective terms have been investigated; the first objective 
term which exploits the spatial relationships of  the 
bits in different positions of  an iris code. The second 
objective allivates the effects of  less reliable bits in iris 
codes By individually or in a combined scheme these 
two objective terms are applied For the optimization 
problem. International Journal of  Engineering Research 
& Technology (IJERT) http://www.ijert.org ISSN: 
2278-0181 IJERTV9IS070414 (This work is licensed 
under a Creative Commons Attribution 4.0 International 
License.) Published by : www.ijert.org Vol. 9 Issue 07, 
July-2020 999 Smereka(Smereka, 2010) , proposed 

a method with the capability of  reliable segmenting 
non-ideal images, which is affected with the issues  like 
blurring, specular reflection, occlusion, lighting variation, 
and off-angle images. For pre-processing the image Haar 
wavelet transform and contour filter were used and to 
detect the edges of  the iris Circular Hough Transform 
and Hysteresis thresholding is used  . ICE database was 
used for experiment to check the performance. Rai et 
al.(Rai & Yadav, 2014),  who proposed a method for code 
matching based on combination of  two algorithms for 
achieving better accuracy and speed rate. Circular Hough 
transform is used to isolate the iris image and then to find 
the blurring and  the zigzag collarette region after that 
verifying and isolating  the eyelids and eyelashes by using 
parabola detection technique and trimmed median filters. 
1-d Log Gabor filters and Haar wavelets are used to 
isolate features from the zigzag and blurr collarette region 
of  iris. To support vector machine and hamming distance 
approach by which Extracted features were recognized. 
Experimental results shows better recognition rate when 
features were extracted from the specific region of  the 
iris, where more complex patterns are available followed 
by combining support vector machines and Hamming 
distance approach for feature recognition. Sunil S 
Harakannanavar1 et al. (Harakannanavar et al., 2018), 
suggested a method were the iris and pupil boundaries 
are determinded by  using circular Hough transform 
and normalization is performed by using Dougmans 
rubber sheet model. The fusion is performed in patch 
level. For performing fusion, the image is converted in 
to 3×3 patches for mask image and converted rubber 
sheet model. Patch conversion is done by sliding window 
technique. So that local information for individual pixels 
can be extracted. The final features of  iris images are 
extracted by block based empirical mode decomposition 
as low pass filter to analyse iris images. Finally the database 
images and the test image are compared using Euclidean 
Distance (ED) classifier.
A facial system has been  proved as one of  the  the 
most securedmethod of  all biometric systems. For high 
level security entirely depending on the system even 
to help  fighting against hacker (Babaei, Molalapata, & 
Pandor, 2012). Requirement of  this identification system 
is simple camera, scanner, ATM within security systems 
that require an identity check. This system obtained by 



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Am. J. Geo Spat. Technol. 3(1) 69-75, 2024

Chinese Academy of  Sciences Institute of  Automation 
CASIA (Raghavendra, 2012). IRIS implementation 
encoded by 1D Log-Gabor filters and phase quantizing 
output produce a bit-wise biometric template. The 
Hamming distance was chosen as a matching metric and 
results show that the proposed approach has a faster 
operation and good recognition performance (Rao, 
Kulkarni, & Reddy, 2012) By comparing two digital eye 
images are capable by using new type of  ATM system 
with Iris Recognition and universal subscriber module . 
IRIS image and the ‘template’ of  ‘IRIS’ is to be compared 
to those in the database (Raj, 2013). In this, we have  tried 
to find a solution of  drastically increase in fraud  through 
ATM by finger biometrics which is possible if  account 
holder is physically present in the ATM booth. Thus, it 
isolates illegal transactions at the ATM points without the 
knowledge of  the authentic owner (Aru & Gozie, 2013).
In this,the given concepts of  face recognition methods & 
its applications. In the future, 2D & 3D Face Recognition 
and large-scale applications such as passport, ID, any 
service, etc. (Parmar & Mehta, 2014).Biometrics refers to 
the quantifiable data related to human characteristics. For 
identification and access control Biometric identification 
is used in computer science as a form. It is also used to 
identify individuals in groups that are under surveillance 
(Betab & Sandhu, 2014).Automatic Teller Machine (ATM) 
in future will have biometric authentication techniques to 
A Review on an ATM with an Eye Koushik S et al. 4 © 
Eureka Journals 2018. All Rights Reserved. ISSN: 2581-
5105 verify identities of  customer during transaction. 
From this, it has been known that biometric ATM systems 
is highly secure with the information of  body part such 
as IRIS which is  easy to maintain and operate with lower 
cost (Malviya, 2014). Biometric technology is proven 
and capable of  high levels of  accuracy and speed. With 
smart cards biometric authentication is highly secured 
and is a stronger method for verification as it is uniquely 
bound to individuals. It is easy to maintain and operate 
with lower cost(Sunehra, 2014).The important step is to 
locate a dominant opensource appearance identification 
program that is used for  local feature analysis and that 
is based on facial verification. This plan must be applied  
on various  multiple systems, involving Windows and 
Linux variants (Suganya & Sunitha, 2015).This research 
has focused on the single biometric trait for recognition 
and authentication. This purpose is to implement the 
biometric security system based on iris and palm print 
recognition using wavelet packet transform and WLD 
with steganography technique for authentication purpose 
(Kamble & Nikumbh, 2015).The face of  each individual 
is antique. Algorithms for face recognition usually use the 
distinguish between different faces. Such facial features 
can be the shape and the distance of  the eyes, eyebrows, 
lips, chin or nose (see [JHP00]). A lot of  research has been 
done in this area in this paper (Das & Debbarma, 2011).Iri 
Recognition biometric Uniqueness may save us  from the 
card theft, Duplication, misplacement anddisclosure of  
password to the unknowns. No excuses for RF/Magnetic 

Cards forget password. No need to further invest on the 
Cards Cost as IRIS biometric is much safer. Biometrics 
allow for increased and frequent security, accountability 
while tracing  and deterring fraud. Proposed method is 
suitable for the ATM users without the need to carry 
ATM card because our eyes going with us(Sainis & Saini, 
2015). The Automated Teller Machine has made life of   
people easier and  the banking industry functions. The 
best case is  still be that the biometric machine password 
only your body that is IRIS recognition method So 
transaction can only be carried out possible due to your 
physical appearance (Mane, Rajeshirke, & Kumbhar, 
2017). New uses like electronic identification cards, which 
are validated with automation, emerge the possible harm 
don’t to a separate cannot be paid back to account, it must 
be prevented biometrics itself  is not the solution to this 
problem. It just provides means to treat the possible user 
candidates uniquely(Bowyer, Hollingsworth, & Flynn, 
2008) ATM provides great services in densed populated 
countries to save time . This identifies a model for the 
modification of  existing ATM systems to economically 
incorporate fingerprint scanning PLUS blood group; and, 
outlines the advantages of  using such system (Gyamfi, 
Mohammed, Nuamah-Gyambra, Katsriku, & Abdulah, 
2016).Many crimes are tampered by stealing someone’s 
password and card which is easier to access the account 
of  user. Traditionally ATM systems authenticate generally 
by using a card (credit, debit, or smart) and a password 
or PIN which no doubt has some defects which caused 
a lot of  the problems for the customers. So we use 
biometric(Lim, Lee, Byeon, & Kim, 2001). This explains 
verifying methods which was inputting owner password 
which is send by the controller. The security features 
were enhanced largely for the stability and reliability of  
owner recognition (Patil, Wanere, Maighane, & Tiwari, 
2013). Iris recognition is a very benefical  and useful 
technique. Iris recognition is highly accurate technique 
due to its specifications and features. This technique is 
tremendously  applicable. This technique has increased 
privacy and identity(Bhagat, Singh, Khajuria, & Student, 
2017) for the user. We are able to understand the meaning 
of  biometrics, its different types in briefing after going 
through the features and specifications. Also, we have 
studied the facial recognition meaning and techniques. In 
the end  we will be able to know a good knowledge about 
the facial recognition (Goel, Kaushik, & Goel, 2012). 
Biometrics is an automatic recognition of  a person based 
on her physiological characteristics and facial recognition. 
We have learnt so many thing about biometricr, this is 
the future of  banking system (Gupta & Sharma, 2013). 
In this, it proves that  how a person can be identified and 
verified  by a numerous of  ways but instead of  carrying 
bunch of  keys or remembering things as passwords. we 
International Journal of  Current Research in Embedded 
System & VLSI Technology 5 Vol. 3, Issue 1 - 2018 © 
Eureka Journals 2018. All Rights Reserved. ISSN: 2581-
5105 can use us as living password, which is called biometric 
recognition technology (Srivastava, 2013).Though there 



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are some flaws of  facial system, there is a scope in India. 
This scheme can be effectively and frequently carried out 
in ATM’s , identifying duplicate voters, passport and visa 
verification, driving permit verification, comparable and 
other written tests, in authorities and personal sectors 
(Garg & Singh, 2014).ATM with Adhaar Card is more 
secure in comparison with other biometric system. As 

above mentioned proposed conceptual model, it has been 
concluded that ATM with Adhaar card systems is highly 
secured as it provides information of  body part i. e. , face 
recognition from three. Different angles. In the end of  
this paper, we will be able to acquire a good knowledge 
about the facial recognition (Gulmire & Ganorkar, 2012)

Figure 3: Techniques and tools for perpetrating ATM frauds

Figure 4: Proposed Approach & Methodology

METHODOLOGY

Fig.4 Block diagram of  iris recognition system First, 
the image is acquired from source. Then preprocessing 
techniques are applied to the images. The preprocessing 
is done to remove noise and blurr from images which 
makes the images more reliable for the training process. 
Pre-processing techniques involve resizing, reduction 
in noise and image contrast. Later the image set will be 
split into 2 sets: train set and validation set. The train set 
will be used for training the model. During the training 
phase, the model will learn the parameter and will try to 
classify the images into the five different classes. Once the 

training is complete, the parameters were tuned to make 
the model more accurate and sizeable. Once the model of  
optimum accuracy is obtained, the made model was used 
to predict some sample images from validation set and 
PNN was used to access the performance.
 
Data Acquisition
The data for training of  a model was obtained from 
a CASIA database v3. There were 22035 images in 
dataset. Highly unbalanced data was obtained through 
this process.For matching purpose we have had 20 



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images in our project. For DATA PRE-PROCESSING  
median filtering was applied  as the noisy images have 
been received. Canny edge operator is used for image 
preprocessing purpose, histogram equalization, threshold 
function for eyelid occlusion & to detect reflection. 

Segmentation
Segmentation is a step where non useful  regions is being 
removed from outside of  the iris. Iris boundaries and pupil 
boundaries are being determined during segementation  
and after that converting this part to a suitable template 
in normalization stage. 

Algorithm 
Pupil Detection Input: Eye Image Output
Pupil Centre and its radius 

i. linear thresholding method is used to create binary 
image of  the input eye. The minimum pixel value of  the 
given image is taken as threshold value.

ii. To remove the smaller parts, median filtering and 
morphological operations are performed on the binary 
image to get the clean region of  Iris for matching. 

iii.We use region properties to Calculate the centred of  
the pupil region .

iv. We perform the following operations to determine 
the radius of  pupil:

• Count the number of  1s present on the Horizontal 
line from centroid to the left side. 

• Count the number of  1s present on the vertical line 
from centroid to the top side. 

• The radius of  pupil is calculated by taking the average.
v. From the calculated centroid and radius segment the 

pupil region from the eye image. ALGORITHM 

Iris Detection Input
Eye image with detected pupil region and its centre. 
Output: Iris centre its radius. International Journal of  
Engineering Research & Technology (IJERT) http://
www.ijert.org ISSN: 2278-0181 IJERTV9IS070414 (This 
work is licensed under a Creative Commons Attribution 
4.0 International License.) Published by : www.ijert.org 
Vol. 9 Issue 07, July-2020 1000
From both side of  the detected pupil we Select two 
rectangle of  small size  
We use canny edge detection method to verify the vertical 
line on the both rectangle and determine the centre point 
of  each of  the line, say p1(x1, y1) and p2(x2, y2). The 
detected line is likely to be on the iris boundaries 
Calculate the distance d1 and d2 of  the point p1 and p2 
from the center. 
The radius of  the iris is obtained by taking the average of  
the distance d1and d2.
With Centroid and radius ,we segment the iris region. 

Normalization
During normalization step,circular iris region that has 
been detected is converted to rectangular shape of  
uniform size. Hough Transform is used to perform this 

process.. The By using Hough transform technique we 
can isolate features of  a particular shape with in an image. 
As its requirement is to isolate desired features must be in 
some parametric form, the classical Hough transform is 
mostly used for the verification  of  regular curves such as 
lines, circles, ellipses, etc. A generalized Hough transform 
can be employed in applications where a simple analytic 
description of  a feature is not possible due to the 
computational complexity of  the generalized Hough 
algorithm. We restrict the main focus of  this discussion to 
the classical Hough transform due to the computational 
complexity of  the generalized Hough algorithm .Beside 
its domain restrictions, the classical Hough transform 
retains many20 applications, as most manufactured 
parts (and many anatomical parts investigated in medical 
imagery) contain feature boundaries which can be 
described by regular curves. The main use of  the Hough 
transform technique is that it abides of  gaps in Iris feature 
boundary verification and is comparitively unaffected by 
image FEATURE EXTRACTION.  Extracting features 
is one of  the important stage in iris recognition system; as 
it is entirely relying on the features that are extracted from 
iris pattern. We have used local binary pattern (LBP) and 
Gray Level Cooccurrence matrix (GLCM).

Feature Extraction
Extracting features is one of  the important stage in iris 
recognition system; as it is entirely relying on the features 
that has been  extracted from iris pattern .We have used 
local binary pattern (LBP) and Gray Level Co occurrence 
matrix (GLCM) CLASSIFIER A probabilistic neural 
network (PNN) has 3 layers of  nodes The architecture 
for a PNN that recognizes K = 2 classes, but it can be 
extended to any number K of  classes. The input layer 
contains N nodes: one for each of  the N input features 
of  a feature vector. These are fan-out nodes that branch 
at each feature input node to all nodes in the hidden 
(or middle) layer so that each hidden node receives the 
complete input
feature vector x. The hidden nodes are collected into 
groups: one group for each of  the K classes.

Classifier
A probabilistic neural network (PNN) has 3 layers of  
nodes.The architecture for a PNN that recognizes K = 
2 classes, but it can be extended to any number K of  
classes. The input layer contains N nodes: one for each 
of  the N input features of  a feature vector. These are 
fan-out nodes that branch at each feature input node to 
all nodes in the hidden (or middle)
layer so that each hidden node receives the complete input 
feature vector x. The hidden nodes are collected into
groups: one group for each of  the K classes.

RESULTS
The Recognition rate, False Rejection rate was calculated 
from CASIA-V3 database. We used 20 images for training 
and 10 images for testing. We also calculated values of



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features such as Contrast, Energy, Homogeneit
Contrast: 0.0017

Energy: 0.9966
Homogeneity: 0.9992

Table 1: False Recognition Rate & Recognition rate values for CASIA database
Database Classifier FRR Recognition rate
CASIA PNN 5.5% 94.6%

Table 2: Comparison of  recognition rate with existing approaches
Method Algorithm Data base Recognition rate
K.Gulmire Global texture feature CASIAvi 89.5%
Mayanak valsa Topological features CASIAvi 92.3%

CONCLUSION
ATM Security System Using Iris Recognition allows the 
genuine and authorize user to access the ATM system. 
Iris recognition system is highly secure as compared to 
any other system present. By identifying and comparing a 
user’s face (IRIS) of  his/her, our system resist suspected 
attackers. In this project, we build a system for ATM 
Security. Images were acquired by database and given 
to the computer system where it is processed by various 
MATLAB functions. Then the database iris image were 
compared with the output iris image and if  it is matched 
then user has access to the account or else
it denies user’s request. Finally, we got a system that has 
recognition rate of  94.6 % using PNN. 

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