Presentation

1. Smart Voting System
Name: Sony Uppar
USN: 2KE21MC052
Guide: Medha K
Assistant Professor
K. L. E. Society’s
K. L. E. Institute of Technology, Hubballi
Dept of Master of Computer Application

2. Contents
• Problem Statement
• Objective
• Scope of the project
• Literature Review
• Hardware and Software Requirements
• Stakeholders/Users
• Functional Requirements
• Non-Functional Requirements

3. • Block Diagram
• Use-Case Diagram
• Screenshots
• Conclusion

4. Problem Statement
Even though our Country has taken steps towards Digitalization of India,
considering the progress of Voting System it still has some flaws. Registration of
Votes is being possible only if people go to polling booths for the current system.
During the time of voting, voter’s name is listed in the list of his/her respective area.
They cannot vote outside the vicinity of the address mentioned in the voting card.
So people who are migrated to other places cannot cast the vote physically. The
recent pandemic situation of Corona Virus shows us the risk of this system. This can
lead to failure of social distancing during voting process, as the voter needs to be
physically present for casting the vote.

5. Objective
• Create a secure online voting system using deep learning and
computer vision technologies.
• Implement facial recognition for voter identification, enhancing
security and preventing unauthorized voting.
• Develop a system that allows voters to cast their ballots from any
location, increasing convenience.
• Automate the vote tallying process to provide instant and accurate
election results.

6. Scope of the project
Describe the project's scope is to create and put into use a comprehensive
smart voting system that makes use of computer vision and deep learning
technology. The system is made to accommodate both users/voters and
administrators, providing each group with special functions. User registration,
secure facial verification and the opportunity to cast votes for the candidates
or parties of their choice are all included in the scope for users/voters. The
technology promises to offer a user-friendly interface that makes it easy and
secure for voters to participate from their preferred places.

7. Literature Review
Title Year Authors Methodology Disadvantages
Decentralized E-
Voting Portal Using
Blockchain
2019 Kriti Patidar,Swapnil Jain blockchain with
homomorphic
encryption
Internet- and blockchain-based
voting systems can have security
risks.
Electronic Voting
Machine with
Enhanced Security
2018 Shashank S Kadam, Ria N
Choudhary,
SujayDandekar, Debjeet
Bardhan, Namdeo B
Vaidya
voting machine
using ATMEGA
32
microcontroller
Security risk present.
Biometrically Secured
Electronic Voting
Machine
2017 RahilRezwan, Huzaifa
Ahmed, M. R. N. Biplob,
S. M. Shuvo, Md.
AbdurRahman
Arduino and
Finger print
scanner
Advanced security system can be
required for significance of
investments and costs to
implement.

8. Title Year Authors Methodology Disadvantages
Real-Time Face
Recognition in
Electronic Voting
System using RFID
and OpenCV
2020 Vetrimani, J. Akash,
C. Rishi, P. Raveena
Face Recognition,
RFID, OpenCV
It explicit
information about
security steps,
which may hinder a
comprehensive
evaluation of the
proposed system's
security and
limitations.
Online Smart
Voting System
Using Image
Processing and
CNN
2022 N. D. Chandar, S.S
Sherkar, G.M Gade,
G.A. Pawar, Prof.
V.N. Dhakane,
Fingerprints
images
matccascad using
CNN.
User must have
reliable
connectivity, User
must have
Hardware

9. Hardware and Software Requirements
Component Specification
Processor I3/Intel Processor
RAM 4GB(min)
Hard Disk 128 GB
Key Board Windows Keyboard Defaults
Mouse Mouse with two or three buttons

10. Component Specification
Computer OS Windows 7 or later
Server-side Scripting Python 3.6
Tools Webyog_SQLyog_Enterprise, xampp-installer,
pycharm-community
IDE: PyCharm IDE
GUI Flask
Libraries OpenCV, Pillow, mysql etc

11. Stakeholders / Users
Citizens, election officials and governmental organizations are the main
stakeholders with an interest in owning the Smart Voting System. Government
agencies and election commissions are important parties with an interest in owning
and using the Smart Voting System. These organizations are under control of
assuring the fairness, transparency and integrity of the voting process.
Media Organization rely on the system for timely and accurate election reporting,
which is essential for informing the public.

12. Functional Requirements
Voter Authentication: This process verifies a voter's eligibility through secure
registration, facial recognition at polling booths, and optional multi-factor
authentication for added security. All personal data, including biometrics like facial
recognition, is encrypted to protect privacy.
Facial Recognition: The system uses advanced techniques, including Convolutional
Neural Networks (CNNs), to accurately identify candidates on the ballot. It maintains
an up-to-date candidate database and ensures real-time candidate recognition for
informed voting.
User Interface: The user interface is designed to be user-friendly, offering clear
instructions for voters and comprehensive tools for election administrators. It provides
real-time updates on voting statistics and issue resolution, enhancing voter
engagement and trust.

13. Non-functional requirements
Performance: The system must efficiently handle a large number of voters, ensuring
prompt and accurate candidate identification. Real-time processing of ballot images
and facial recognition with minimal delays is essential.
Security: Protecting private voter information and the integrity of the voting process
is paramount. The system employs strong encryption to safeguard biometric data
and access controls to prevent unauthorized entry.
Maintainability: The system's long-term sustainability and effective management
rely on maintainability. A well-structured, modular, and well-documented codebase
simplifies updates and revisions.

14. Block Diagram

15. Use Case Diagrams
• Use Case Diagram for Admin
Admin is the person in charge and maintaining the
system entirely.
Login: Logs into the system.
Select Candidates: Selects the candidates for
voting.
Train: Train a model with the captured images of
the voter
View: View voting results.
Logout: after completing the process he log’s out
from the system.

16. • Use Case Diagram for User
User is the person who is interested to vote we
can call him as an voter.
User Register: Register into the system if he is
new otherwise he log’s in directly.
User Login: Login into the system using facial
images or Video Stream.
User vote: They vote to their desired
party/candidate.
View: View voting results.

17. Screenshots
Home page

28. Conclusion
Successfully developed an online voting system. The system has a new registration
feature which takes in frontal facial images of the person registering. The user needs
to verify their emails using OTP as well for a successful registration. Once someone
is registered, the models has to be trained again by the admin in order to detect and
recognize the new person. A registered user is identified by their face and then
allowed to vote unless they have already voted as no one can vote more than once.
Frontal Face Haarcascading is used for facial embedding generation. Computer
Vision is employed for image preprocessing and video streaming. Flask is used for
the User Interface via Python.

29. Thank You !