It is AI based face recognition system in mobile banking.

1. FACE RECOGNITION BASED
MULTIFACTOR AUTHENTICATION
METHOD FOR ONLINE BANKING
BY
AKASH K
KAMELESH P
KALIDHAS S
MOHAMED ASHRAF ALI M
GUIDE NAME : NARMADHA S

2. DOMAIN ARTIFICIAL
INTELLIGENCE

3. NET BANKING WITH FACIAL
AUTHENTICATION

5. FACE RECOGNITION BASED
MULTIFACTOR AUTHENTICATION
METHOD FOR ONLINE BANKING

6. ABSTRACT
• Authentication is an essential thing, which prevents unknown person in a
computer based environment system.
• Once malicious user logs into account he has full access to all services of
registered user.
• To overcome the malicious access in Online Banking system, here propose a
multi layer authentication process.
• Multi layer combines the process of Illusion based user authentication,
Brightness password, Face biometric verification and OTP verification.
• Notifications are send to the user regarding banking interface access and
amount transaction with multiparty access system

7. EXISTING SYSTEM
• In a smart card based remote user authentication, an authorized registered
user and a remote server need to authenticate each other in order to make
secure communication.
• After mutual authentication, both the communicating parties establish a
session key which can be further used to secure communication among them
for accessing the services from a remote server by a legal user.
• To provide untraceability feature, which is a very important feature in a user
authentication protocol.

8. DISADVANTAGES
• Conventional methods of authentication via usernames and passwords are no
longer sufficient.
• Difficult to analyze the original users.
• Multi-person access may be fraudulent.
• SMS alert only provide for current transactions, difficult to know the
specific persons.

9. PROPOSED SYSTEM
• Implement real time authentication system using hybrid PIN, Bright
password, face biometrics for authorized the person for online banking
system.
• Face biometric can be used to provide cost effective rather than other
biometric features such as fingerprint, iris and other features using
Grassmann algorithm.
• And also provide multiparty access system to allow the multiple persons to
access the same accounts by providing access privileges.
• The OTP based password can be send at the time transactions.

10. ADVANTAGES
• Solve transaction attacks in banking interface
• Without primary user knowledge, no one perform fraud activities
• High level authentication steps for access financial transactions
• Notification about transactions
• No need to implement additional sensors

11. ARCHITECTURE DIAGRAM

12. LITERATURE SURVEY
Title Year Author Technique Merit Demerit
AI based
Technologies for
Digital and Banking
Fraud During Covid-
19
2022 Priya Makhija Artificial
intelligence
technique
Helps to
identify and
eliminate the
fraud
Malware attacks
can be occurred
Leveraging
Blockchain
Technology
for Internet
of Things Powered
Banking Sector
2022 R Anuradha Block chain
technology
Provides a
realistic
solution
Implementation
cost is high

13. LITERATURE SURVEY
Title Year Author Technique Merit Demerit
Difference co-
occurrence matrix
using BP neural
network for
fingerprint liveness
detection
2019 Yuan,
Chengsheng
Detect
fingerprint
liveness based
on BP neural
network
Provide a better
detection
accuracy
Pre-trained
network model is
not able to depict
the original
fingerprints.
Efficient and secure
biometric-based user
authenticated key
agreement scheme
with anonymity
2018 Kang,
Dongwoo
Authenticated
key agreement
method
Improves the
security level
also ensures
efficiency.
This method may
cause time
synchronization
problem between
servers and users

14. LITERATURE SURVEY
Title Year Author Technique Merit Demerit
A novel weber local
binary descriptor for
fingerprint liveness
detection
2018 Xia, Zhihua,
Chengsheng
Weber local
binary
descriptor for
fingerprint
liveness
detection
(FLD)
Proposed
method
obtains the
best detection
accuracy
This greatly
increases the
cost of the
recognition
system.
Cloud centric
authentication for
wearable healthcare
monitoring system
2018 Srinivas,
Jangirala
Real-Or-
Random
(ROR) model
It can be
protected
against
passive and
active attacks
The location
privacy of the
reader in these
protocols is not
protected.

15. LITERATURE SURVEY
Title Year Author Technique Merit Demerit
Malware Detection
and Prevention using
Artificial
Intelligence
Techniques
2022 MD Jobair
Hossain Faruk
Artificial
neural
network
Prevents from
malware
activities
Support only
datasets in
banking
transactions

16. ALGORITHM
• Grassmann Algorithm
• The set of m-dimensional linear subspaces of the R D is known as G(m, D).
• The G(m, D) is a compact Riemannian manifold with m(Dm) dimensions.
• An orthonormal matrix Y of size D by m can be used to represent an element of G(m,
D), with Y = Im, where Im is the m by m identity matrix.
• The matrices Y1 and Y2 are considered the same if and only if span(Y1) = span(Y2),
where span(Y) signifies the subspace spanned by the column vectors of Y.
• The length of the shortest geodesic connecting two points on the Grassmann
manifold is the Riemannian distance between two subspaces.

17. ALGORITHM
• Grassmann Algorithm
• Input: A set of P points on manifold
• {Xi}i=1
P
∈ G d, D
• Output: Karcher meanμK
• 1. Set an initial estimate of Karcher mean μK = Xiby randomly picking one point in
Xi}i=1
P
• 2. Compute the average tangent vector
• A =
1
P i=1
P
logμK Xi
• 3. If A < 𝜀 then return μK stop, else go to Step 4
• 4. Move μK in average tangent direction μK = expμK αA , whereα > 0 is a
parameter of step size. Go to Step 2, until μK meets the termination conditions
(reaching the max iterations, or other convergence conditions

18. MODULE LIST
• Bank interface creation
• User registration
• Hybrid PIN and Bright password verification
• Face verification
• Multipart access system
• Alert system

19. SYSTEM SPECIFICATION
• Hardware Specification
• Processor : Intel processor
• RAM : 1GB
• Hard disk : 160 GB
• Compact Disk : 650 Mb
• Keyboard : Standard keyboard
• Monitor : 15 inch color monitor
• Software Specification
• Operating system : Windows OS
• Front End : PYTHON
• Back End : MYSQL

20. REFERENCES
• [1] Sinha, Mudita, Elizabeth Chacko, and Priya Makhija. "AI Based Technologies for Digital
and Banking Fraud During Covid-19." Integrating Meta-Heuristics and Machine Learning
for Real-World Optimization Problems. Springer, Cham, 2022. 443-459.
• [2] Surekha, Nayak, et al. "Leveraging Blockchain Technology for Internet of Things
Powered Banking Sector." Blockchain based Internet of Things. Springer, Singapore, 2022.
181-207.
• [3] Garg, Yashika, And Kanika Sachdeva. "Artificial Intelligence In Indian Banking Sector:
A Game Changer." DogoRangsang Research Journal, Vol-12 Issue-08 No. 05 August 2022
• [4] Jobair Hossain Faruk, Md, et al. "Malware Detection and Prevention using Artificial
Intelligence Techniques." arXiv e-prints (2022): arXiv-2206.
• [5] Priya, G. Jaculine, and S. Saradha. "Fraud detection and prevention using machine
learning algorithms: a review." 2021 7th International Conference on Electrical Energy
Systems (ICEES). IEEE, 2021.

21. THE END