Attendance Management System

This project is an Attendance Management System that uses facial recognition to mark attendance. It is built using OpenCV, Streamlit, and other Python libraries.

Features

• Register new students with their name and roll number.
• Capture images of students for training the facial recognition model.
• Train the facial recognition model using the captured images.
• Recognize faces in real-time and mark attendance.
• View the student and attendance databases.

Requirements

Install the required Python packages using the following command:

pip install -r 

requirements.txt

Usage

Register a Student

1. Run the Streamlit app:

   streamlit run app.py

2. Select "Register Student" from the sidebar menu.
3. Enter the student's name and roll number.
4. Click "Register" to capture images and train the model.

Mark Attendance

1. Run the Streamlit app:

   streamlit run app.py

2. Select "Attendance" from the sidebar menu.
3. Click "Start Attendance" to start the live face recognition and mark attendance.

View Databases

1. Run the Streamlit app:

   streamlit run app.py

2. Select "View Databases" from the sidebar menu.
3. View the student and attendance databases.

File Structure

• app.py: Main Streamlit app file.
• takeimages.py: Script to capture images of students.
• train.py: Script to train the facial recognition model.
• test.py: Script to recognize faces and mark attendance.
• requirements.txt: List of required Python packages.
• students.csv: CSV file to store student details.
• attendance.csv: CSV file to store attendance records.
• TrainingImage: Directory to store captured images.
• TrainingImageLabel: Directory to store trained model and label files.

Acknowledgements

• OpenCV for providing the tools for image processing and facial recognition.
• Streamlit for creating an easy-to-use web interface.