IRJET- Secure Online Payment with Facial Recognition using CNN

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 604
Secure Online Payment with Facial Recognition using CNN
Ritika Patil1, Shubhada Patil2, Shruti Sagar3, Shruti Sancheti 4, Prof. Sheetal More5
1,2,3,4Student, Dept. of Computer Engineering, Sinhgad College of Engineering, Maharahtra, India
5Professor, Dept. of Computer Engineering, Sinhgad College of Engineering, Maharahtra, India
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Abstract - The recent advancements in technologyhaveled
to a surge in online transactions via online shopping, internet
banking, payment gateways, etc. Security is one of the major
issues during these transactions. Due to such issues peopleare
hesitant to use online transactions, so we propose our system
which secures online transactions using two-step verification.
The first step is OTP verification followedbyfacialrecognition.
The system uses an online interface in order to interact with a
user. The interface is used to get card details from the
particular user. After the OTP verification, the user will be
authenticated using facial recognition. The systemusesaCNN
in order to verify the user by comparing therealtimecaptured
image of the user against the images associated withtheusers
account.
Key Words: CNN, online payment, security, image
verification, face recognition, credit card
1. INTRODUCTION
Today’s advanced technology allows hackerstogetpersonal
details of users and so some people are reluctant to use
online transactions. This makes security a key factor during
online payments. We propose a system to enhance the
security of online transactions by providing a two-step
verification process-OTP verification followed by Facial
Recognition.
The system will focus on reducing attacks, which makes the
transactions vulnerable, like lost or stolen cards, account
takeover, counterfeit cards, fraudulent application, multiple
imprint, and collusive merchants.Inaccounttakeover,a card
holder unknowingly gives personal detailstoa fraudsterand
the fraudster then issues a new card using these details. In
counterfeit, a card is cloned and used by a fraudster. In
multiple imprints, as singletransactionsisrecordedmultiple
times. In collusive merchants, employees work with the
fraudsters. The system succeeds in reducing all these frauds
by capturing and verifying a real time image of the card
holders.
Authentication with the help of biometrics is gaining
popularity due to its uniqueness for every person. The
different biometric technologies are fingerprint, hand
geometry, iris, face and palm. We are using face recognition
is the most popular due to its usability, collectability and
acceptability [8]. There are many techniques used for facial
recognition like SVM [2], PCA [2], LDA [3], and CNN. The
system uses CNN for facial recognition because it has shown
better results [1] for face recognition. The system has a web
user interface. It is compatible with all operating systems
and all types of browsers. The user must have a camera
connected to the machine in order to capture a real time
image. It also requires that the user have a good internet
connection to access the UI.
2. RELATED WORK
2.1 Techniques for securing online transactions
The current methods of securing online transactionsinclude
account associated password, CVV and OTP. One Time
Password (OTP) is a set of alphanumeric characterswhichis
sent to the account holders registered phone number
through SMS or e-mails. The person who does not have
access to these will not be able to follow through with the
online transaction. Although it seems like OTP is secure and
safe, it is not robust to attacks like impersonation, phishing,
and malware based replay attacks.
2.2 Techniques for biometrics
Biometrics technology is used for authentication of an
authorized user. The different biometric techniques include
voice, face [9], palm and fingerprint. Voice recognition
measures a users voice patterns, speaking style and pitch.
Fingerprint identification uses patterns of the rides and
valleys present in fingerprints scanned beforehand. Palm
identification uses palm prints and other physical traits for
unique identification of user’s palm. Face recognition
captures and stores the facial features of an individual and
stores them for identification process.
2.3 Techniques for face recognition
The different techniques used for face recognition include
PCA [2], LDA [3], SVM [2] and CNN. Principle component
analysis (PCA) is used to reducethedimensionalityofdata to
reduce the number of parameters in images, which are high
dimensional correlated data. It is based on Eigen values and
Eigen vectors. Linear Discriminant Analysis (LDA) is based
on linear projection ofimage spacetolowerdimensionspace
by maximizing between-class scatter and minimizing the
within-class scatter [3]. Support Vector Machine (SVM) is
used for binary classification. AnSVMbasedclassifierisused
in face recognition to predict the similarity and dissimilarity
between two images [2]. Convolutional Neural Networksisa

deep neural network architecture which is used to extract
features from images. CNNs can be used as classifiers or
simply feature extraction. CNNs have show better accuracy
with facial recognition as compared to the other techniques.
3. LITERATURE SURVEY
3.1 FaceNet (2015 IEEE): A Unified Embedding for
Face Recognition and Clustering
In this paper is presented a system, called FaceNet [1].
FaceNet learns how to directly map faceimagestoa compact
Euclidean space. The distances between the generated
vectors give the similarity between the faces. The created
space can be used for differenttaskssuchasfacerecognition,
verification and clustering using standard techniques with
FaceNet embeddings as feature vectors. We extend this
concept to apply it to secure online transactions.
3.2 When Face Recognition Meets with Deep
Learning (2015 IEEE)
The paper [4] aims to provide a common ground to all
students and researchers alike by conducting an evaluation
of easily reproducible face recognition systems based on
CNNs. It uses public database LFW (Labelled Faces in the
Wild) to train CNNs instead of a personal database. It
proposes three CNN architectures which are the first
reported architectures trained using LFW data. We use the
LFW dataset to train our network as well as a personal
database to test it.
3.3 Building Recognition System Based on Deep
Learning (2016 IEEE)
Deep learning architectures use a multiple convolution
layers and activationfunctionswhicharecascaded.Themost
important aspect is the setup-the number of layers and the
number of neurons in each layer, the selection of activation
functions and optimization algorithm. It [5] uses GPU
implementation of CNN. The CNN is trained in a supervised
way in order to achieve very good results. We extend this
system in order to use it for the secure online transactions.
3.4 An Efficient Scheme for Face Detection (2015
IEEE)
This work [6] is based on skin colour, contour drawing and
feature extraction to provide an efficient and simple way to
detect human faces in images. The features under
consideration are mouth, eyes, and nose. The results are
with good accuracy, great speed and simple computations.
3.5 Gender and Age Classification of Human Faces
(2017 IEEE)
This paper [10] introduces an approach to classify gender
and age from images of human faces which is an essential
part of our method for autonomous detection of anomalous
human behaviour. This paper is a continuous study from
previous research on heterogeneousdata inwhichimagesas
supporting evidence is used. A method for image
classification based on a pre-trained deep model for feature
extraction and representation followed by a Support Vector
Machine classifier is presented. We use CNN in place ofSVM.
3.6 Credit CardTransactionUsingFaceRecognition
Authentication (2015 ICIIBMS)
This paper [8] is based on a credit card transaction system
which integrates face recognition and face detection
technology using Haar Cascade and GLCM algorithms. The
training data set includes the extracted features from the
images and is stored in administrator database, which is
then used for the authentication. We use CNN instead to
increase efficiency and reduce complexity of system.
4. SYSTEM ARCHITECTURE
Fig -1: System Architecture
The system has the following components-Database, Client,
Server and CNN. The database is divided into training and
testing dataset. The training dataset, LFW, is used to train
the CNN component while the testing dataset, a dataset of
personal faces, is used to test its accuracy. The Server
includes the modules used to interact with the database.
They are also used to process the images received from the
CNN. The Client side has the initiate payment, get card
details and capture image modules. The capture image
module is used to capture the image of the user and send it
to the server side. The CNN consists of the convoluted layer
which is used to extract the features of the user, the pooling
layer which is used to reduce image size and the fully
connected layer to give the probability of the user being the
actual user. The system works as follows. A user initiates

payment by entering the card details in the displayed web
page. The details are sent to server side and are
authenticated. The user is then redirected to OTP web page.
After entering a valid OTP, the user is redirected to the
capture image web page where a real time image of user is
captured and sent to server side. The image is processed via
the CNN and authenticates the user against the image of the
user stored in database at the server side.
4. METHODOLOGY
Convolutional Neural Network is being used for facial
recognition and authentication of the user. A CNN is a deep
learning algorithm and is found to be efficient in analysing
images because CNNs use relatively little pre-processing
compared to other image classification algorithms [4]. A
CNN consists of an input and an output layer and hidden
layers. A deep CNN architecture has a series of hiddenlayers
one after the other where the output of one hidden layer is
sent as input to the next hidden layer. The different layers
present in the hidden layers are convolutional layer,pooling
layer and normalization layers, and activation functions.
The system uses the inception v3 model [7] for CNN
architecture. The inception model includes multiple
inception blocks, each of which consist of a combination of
Convolutional layers, pooling layers and activations which
are concatenated together.
A Siamese network is used in our system. The Siamese
network uses two CNNs, with the same weights and bias.
One CNN gets its input image from the database and the
other CNNs input is the real time captured image of theuser.
The model uses the triplet loss function tofindthedifference
between the outputs of both the CNNs. This difference is
used for training the complete model using back
propagation.
A CNN takes an input image at its input layer. It extracts
various features of the input and sends it to the following
layer, which is usually a convolutional layer.
The Convolutional Layer performs mostofthecomputations
in the CNN architecture. Each convolutional layer takes an
input image. The image has several parameters like size of
image, number and size of the filter being used. The filter is
used to extract the useful features from the input images. It
is usually an odd square matrix which traverses along the
height and width of the input image. The output of one
convolutional layer acts as input to the following layer.
The Pooling Layer is used to reduce the number of
parameters of the input image to make computations more
efficient. There are several activationsthatcanbeusedinthe
Pooling Layer. The system uses max pool function to reduce
the parameters and the output generated is sent as input to
the next layer.
The Output Layer is a fully connected layer where every
neuron of previous layer is connected to every neuron of
current layer. The fully connected layer first uses the flatten
function to flatten the input image. This is then sent as input
to an activation function such as sigmoid function to predict
the output class of the image. The system uses CNN to
encode the images and then find the difference between the
flattened encoded images to see if they are match. The
encoded images match if the difference is less than a learned
threshold.
Back propagation is used in order to train the CNN and
reduce the errors present in the network. The system uses
the triplet loss function to adjust the weights and bias in
each layer.
5. RESULTS
The training of the CNN on our personal dataset of faces
resulted in an accuracy of 80-85%. The system is able to
correctly identify the users, on which the network has been
trained, and authenticate them in the real time application.
6. CONCLUSION
The system enhances the security of online transactions by
successfully recognizing and authenticating authorized
users. The system can be used as a payment gateway for any
application which requires online payments. These include
ecommerce websites, internet and mobile banking. The
system can be accessed on any operating system using any
web browser. For future work, iris identification can be
added to the system for further enhancing thesecurityofthe
transactions.
ACKNOWLEDGEMENT
We would like to thank our guide, Prof. S.V. More, for her
constant support and guidance. We would also like to
express our deep gratitude to the principal, Dr. S.D.
Lokhande, for his continuous efforts in creating a
competitive environment in our college. We would like to
convey our heartfelt thanks to our H.O.D., Prof. Wankhade,
for giving us the opportunity to embark upon this topic. We
also wish to thank all the staff membersoftheDepartmentof
Computer Engineering for helping us directlyorindirectlyin
completing the work successfully.
REFERENCES
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