Facial Recognition Secure Voting System with Website Interface
- -It will have user and admin module.
- -It will have a face recognition feature using Deep Learning.
- -In the user there will be a feature to vote for candidates and view results.
- -The admin will have the facility to add candidate users and announcements results view voting statistics.
Online Voting System using Facial Recognition, this project developed using python programming language and Django framework is used for creating graphical user interface. In terms of algorithm, we used DLIB and CNN; Dlib Algorithm helps to identify the landmarks of face with their values proper face structure. For some uniqueness in project, we have used Blockchain Technology. When user the python code then it will generate URL, and that can open webpage. In this project we used SQLite database to stored data. This project model will first capture image, then from that particular image we have to detect the face through FaceNet. FaceNet is a accurate algorithm, in neural network which basically predict face from image in very accurate manner, and Dlib is used for identify face landmark store landmark. And final those landmark we have trained model through CNN.
Project Working of Facial Recognition Based Voting System:-
- User have to first create their profile as a Candidate, they have to enter their personal details and also stored voter face in database.
- User can also create their position with Name and Max Vote.
- Voters section where user can see the voters details.
- Votes page will show with their Name and Voted for and position.
- Dashboard will show the graph of Votes Tally, Number of Position, Candidates, Total Voters and Voted.
Online Voting System using face Recognition in Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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