Utilizing Python's OpenCV libary, a facial recognition system software has been created for an innovative and modern way of securing your vehicle.
Android Studio has been used to create a functional mobile application, giving the user engine, lock, and alarm control.
A Raspberry Pi 4 processor and Firebase's Realtime Database have allowed us to crate a fully functional real-time prototype.
Autonomous, energy efficient vehicles are the next big thing in the car industry. It only makes sense that the security system moves with the times.
Sam Hermas Parada
Based on the high speed network connection, more and more device are connected to internet and controlled by mobile phones. Our project is helping the customer to enjoy the true keyless to their life and make no key driving be a part of their smart home life.
Leyao Li
Our project is important because it is protecting the user’s car through a simple face recognition program that is innovative and efficient.
Batuhan Basoglu
The Anti-theft system works with the help of sensors and cameras in and installed around the vehicle, we are able to provide more security to our customer.
Qian Ma
Our project has 3 main advantages: 1. More convenient and good security: Personal facial images are not easy to be obtained by the outside world, lower probability to lost or stolen than physical keys. 2. Non-contact: users do not have to physical touch the acquisition device (camera) to do the facial authentication. 3. Visualization: the thief's face can be traced.
Fayer Zhang
As technology advances so do our cars. A prime example is Tesla innovating the Automotive industry with self driving smart cars. Our Anti Theft Facial recognition is just another way we can push technology to make cars smarter and safer for customers.
Alexandre Pereira
The objective of this project was to create a vehicle anti-theft recognition system that would be practical, secure, and innovative. It was designed to prevent burglary with the use of facial recogniton systems. Python's OpenCV library was used to implement the facial recognition system. A Firebase Realtime Database was utilized to store the profiles of drivers and thieves; this would be updated in realtime. An application was created on Android Studio so the product could be used in a practical settting. A functional prototype consisting of a raspberry pi, engine, and wheels has been demonstrated to the Professor's and Teaching Assistant's of our capstone project.
We see this as a practical solution in an innovative manner for the future of reducing the risk of car robberies. Each of our team members have applied +4 years of Computer Engineering knowledge to work on the completion of this project based on a real-world problem.
75 Laurier Ave.
Ottawa, ON
Canada