FaceTrack

An AI Based Facial Attendance System developed in 1 day for the T-Hunt Hackathon, Manipal University Jaipur.

Demo Video

Below is the video demonstrating our system i.e. our Web App:

demo.mp4

Prerequisites to deploy and use the Web App

• Python should be installed on the system to run the Backend of the Web App.

• Pip should be installed on the system to install dependencies of the Back End of the Web App.

• The following packages should be installed by using pip on the system (run the following commands in Command Prompt/Terminal after installing python and pip to install them):

  pip install tornado
  pip install opencv-python
  pip install opencv-contrib-python
  pip install numpy
  pip install Pillow

Instructions to deploy and use the Web App

• Donwload the project and extract the files in any folder.

• Open Command Prompt/Terminal and navigate to the "Backend" folder.

• In the "Backend" folder, run the "app.py" file to start the Facial Recognizer AI WebSocket Server by running the following command:

  python app.py

• Then, open a new instance of the Command Prompt/Terminal and navigate to the "Frontend" folder.

• Run the following command in the "Frontend" directory to start the Frontend HTTP Server:

  python -m http.server 5000

  Note: here 5000 is the port at which the HTTP Server will run and you could set it to anything you want.

• Done! The Web App is now deployed and can be accessed at http://localhost:5000 (replace 5000 with the port which you used in the previous command).

• In the Web App, first go to the Register page to register yourself in the System.

• Then, go to the Mark Attendance page to mark your attendance for the day.

Limitations of the project

• Since the whole project was developed on a Laptop, the training capabilties of the model was restricted and hence is trained using merely 100 images per student which is pretty low as compared to the standard training requirement.
• Even then it has a fairly high accuracy. But due to lack of proper model training, it can confuse between students and identify them incorrectly.
• Due to restricted computing power, it can take longer than usual by the model recognizer to recognize a student.
• We will overcome and fix these limitations in the near future, once we get the resources needed to do the proper model training and faster recognition.

Credits for the Project

• Mr. Abhay Tripathi (@abhaytr):

  • Developed the complete Backend i.e. made the model trainer and model recognizer using OpenCV standard facial recognizer.
  • Developed the WebSocket Server to receive and send facial data in the form of Base64 Images.
  • Developed the SQLite Database on the backend to store registered students data and to mark and store the attendance of each student per day.
  • Developed the Frontend Script using JavaScript to take camera input from the client browser to get facial data and integrated it with the WebSocket Server by creating endpoints on the server for registration and marking attendance i.e model training from the received facial data from the registration page and then student recognition from the received facial data from the mark attendance page in the form of Base64 images.
  • Developed the Frontend UI using HTML and Bootstrap CSS Framework to take Camera Input and User Data from the client browser in the Registration and Mark Attendance Pages, and also to do facial recognition on the frontend using OpenCV.js framework.

• Mr. Nikhil Dixit (@nikdixit):

  • Developed the Home Page of the Frontend using HTML and Bootstrap CSS Framework.
  • Developed the Frontend of Register and Mark Attendance Pages using HTML and Bootstrap CSS Framework.