Urban Hawx

An example of people getting identified in our security system

Inspiration

Home security in today's day and age is something of paramount importance. However, traditional locks and keys are getting bypassed too easily, and normal CCTV cameras are not able to distinguish between people, thus people are able to break into homes easily enough.

That is what inspired us to create this facial detection and recognition software which would be able to recognize who is safe, who isn't and notify users based on the system's status.

What it does

This is a facial detection and recognition software which allows the system to distinguish between a safe, registered user and an unknown one, in a live feed. The system uses eye, face and smile detection to analyze and differ between different people. The rectangles around each person's face would have two things, the person's name and confidence rating for that choice. The system also has two modes: a home mode and an away mode, and it allows a person to add their face and name as a registered user in the home mode.

How we built it

We used Python and OpenCV for the Back-end work, and Flask for the Front-end work.

Accomplishments that we're proud of

We achieved everything we aimed for, and we're really proud of the reliability of our facial recognition system.

Built With

Submitted to

HackIllinois 2022

Created by

Along with the original idea for the project, I worked on the Facial Detection and Recognition aspect of it. Beyond that, I ensured that the camera would function properly and detect, recognize and display rectangles based on a person's face structure, eyes, and smile. Throughout this project, I was working with Python and OpenCV to create the Back-end of the project.

Himnish Jain
I ,along with Kushal, worked on the back-end side of the project. We were responsible for collecting data using the webcam and capturing multiple pictures of the user’s face to build a database. Our work also entailed training our model around the database. We also worked on recognition to compare a user’s facial match with an existing entry in the database. We did this using Python and OpenCV.

Ribhavs2
I worked on the back end side of the project. I, along with Ribhav, recognised the faces detected by the software designed by Himnish and stored them in our database. We used the database to train our model to increase accuracy of the recognition.

kushal238 Agarwal
I worked along with Apoorva to create the full front end and it’s integration with backend using Flask

Malay-Rungta
Apoorva Aditya