Face Recognition Attendance System

Overview

The Face Recognition Attendance System is a Python-based application that uses OpenCV, Tkinter, and other libraries to take images of students, train a machine learning model on the images, and track student attendance based on face recognition. It allows users to:

1. Capture face images of students and save them.
2. Train a model to recognize faces.
3. Track student attendance and log it in a CSV file.

Features

• Capture Student Faces: Takes images of students for training the face recognition model.
• Train Model: Trains the model using the images captured, allowing the system to recognize students' faces.
• Mark Attendance: Marks student attendance by recognizing faces during real-time video capture.
• Save Attendance: Saves attendance data in a CSV file.
• GUI Interface: The application uses Tkinter for a user-friendly interface.
• Notification System: Displays real-time notifications to the user on various actions, such as saving images or training the model.

Requirements

Before running the application, make sure you have the following Python libraries installed:

• opencv-python
• Pillow
• pandas
• numpy
• tkinter (Usually pre-installed with Python)
• csv
• datetime

To install the required libraries, run:

pip install opencv-python Pillow pandas numpy

Files

1. StudentDetails.csv: Stores student roll numbers and names.
2. TrainingImage/: Directory where student face images are saved.
3. TrainingImageLabel/Trainner.yml: Saved machine learning model for face recognition.
4. Attendance/: Directory where attendance logs are saved.
5. haarcascade_frontalface_default.xml: Pre-trained classifier for face detection (downloadable from OpenCV repository).

Usage Instructions

1. Start the Application: Run the script to launch the Face Recognition Attendance System.

   python attendance_system.py

2. Capture Student Faces:

   • Enter the Roll No and Student Name in the provided fields.
   • Click on Take Images.
   • The system will open a webcam window, and the student will need to stay in front of the camera for face detection.
   • Once enough images are captured, the system will notify the user.

3. Train the Model:

   • Click on Train Model to train the face recognition model using the captured images.
   • The model will be saved as Trainner.yml for future use.

4. Mark Attendance:

   • Click on Mark Attendance to start face recognition.
   • The system will use the webcam to detect faces in real time and match them with the trained model.
   • Attendance will be saved in a timestamped CSV file under the Attendance/ folder.

5. Clear Fields:

   • Click on Clear to reset the input fields and messages.

6. Exit:

   • Click on Quit to close the application.

Directory Structure

- Face_Recognition_Attendance_System/
    - StudentDetails.csv              # Stores student details (Roll No and Name)
    - TrainingImage/                  # Captured images of students' faces
    - TrainingImageLabel/             # Folder where the trained model (Trainner.yml) is stored
    - Attendance/                     # Folder to store the attendance logs
    - haarcascade_frontalface_default.xml  # Pre-trained face detection model (downloadable from OpenCV)
    - attendance_system.py            # Python script to run the system

Notes

• Roll No should be numeric, and the Name should only contain letters and spaces.
• The system uses LBPH (Local Binary Pattern Histogram) face recognition for training and prediction.
• The system assumes a webcam is available for face capture and recognition.
• The application saves attendance in a CSV file named Attendance_DDMMYYYY.csv.

Troubleshooting

• Ensure that the webcam is accessible and working properly.
• If the haarcascade_frontalface_default.xml file is missing, download it from here.
• If the StudentDetails.csv file is not found, the system will display an error message. Ensure the file is in the same directory as the script.