Class Attendance Management System

About

This is a class attendance management system based on face recognition technology. It uses pre-trained opencv caffe models to detect and capture the faces of students which are then saved as images. This is the enrollment phase.
In the training phase, these images are passed to a model which extracts the feature vectors(embeddings) that quantify each face in the image, a labelEncoder function is then used to add names to the embeddings which are subsequently used to train a Support Vector Machine (SVM) learning model.
When the 'Take Attendance' process is initiated, the system once again captures the faces of students in the class at that point in time and the trained SVM model is used to determine who, by percentage probabilities, is in the new captured images. This percentage probability is compared to a predefined threshold(confidence level) and if it meets it, the labelEncoder is queried for the most likely name. This is the recognition phase.
When a student's face is recognized/authenticated, his/her attendance record is updated accordingly.
Attendance records as well as records of enrolled students can also be viewed and modified on the system.

Installation

This project can be installed by running the following commands in your preferred directory.

$ git clone https://github.com/The-Professor99/CAMSyFReT.git

The project can also be installed using pip. Visit this page for guidelines.

Requirements

This assumes you have Python installed on your system

Setup a virtual environment and activate it

Run the command below in the project's root folder to install the requirements.

$ pip install -r requirements.txt

Using The Executable File

If you don't have python installed on your system or you simply want to use the project without having to run any command, don't worry, we've got you covered

After cloning this repository, simply navigate to this startup file and double click on it to get the project running.

Please note that this executable file may only work on *NIX operating systems

How To Use

Activate the virtual environment - Discard this if you're using the executable file

Run the below command to start up the project - Discard this if you're using the executable file

$ python run.py

Upon successful login, you can enroll a student through the Enroll Student tab.

Enroll student(s)

Fill in the student's details.
click on Capture biometrics
When the camera comes up, take about 10 pictures of the student. Ensure that the face is detected. To take a picture, press k and to exit the camera, press q.
Train the model on captured faced by pressing the Train Model button. You only need to train the model only when you newly enroll a student.

Take Attendance

After the above section is completed, whenever you want to take an attendance, place your camera or webcam at a position where it can easily detect the faces of students and press the Start recognition or Take Attendance button.
The system will find and update the attendance records of students it detects and recognize.
At the end of the attendance taking process, press q to exit the camera.

View Attendance Records.

click the view attendance tab buttons to view all attendace taken records.
Enter course details and click on view course details button to find details of all students you've enrolled into the course.
You can make changes to these records and after making the changes, hit the Save modifications button to save them.

Deployment

The project has been published to Pypi and can be accessed here

Issues

Trying to take an attendance or capture biometrics may shutdown the app, if this is the case on your system and you receive an error messaging on the terminal stating: "can't open camera by index", please attach a webcam and try again.
If an error occurs when trying to train the model on captured images, go to the settings page and click on reset. After this, capture the images again and try training the model again.

Other Details

Some UML diagrams drafted out in the course of running the project can be accessed here
Make sure to train models on captured faces before calling on take attendance.
The models determine if a face is present in the image and whose it is by generating percentage probabilities. These percentage probabilities are compared to a predefined threshold(confidence level) which, by default, is set to 0.80. You can increase/decrease these confidence levels in the settings page to limit the occurrencies of false positives and negatives.

Useful Resources

The fastest way to learn OpenCV, Object Detection, and Deep Learning

The-Professor99/CAMSyFReT