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Face Recognition-based Attendance System

A face recognition-based attendance system designed to automate attendance marking. This system captures facial images and matches them against a database of registered users for accurate tracking.

Features

  • Automated Attendance: Captures and identifies faces to mark attendance without manual intervention.
  • User Database: Compares captured images with registered user data for verification.
  • Training Module: Generates training data for improved recognition.
  • Real-time Detection: Utilizes computer vision for face detection and attendance recording.

Technologies Used

  • Python: Core programming language.
  • OpenCV: Real-time face detection and image processing.
  • NumPy: Data manipulation and processing.
  • Haar Cascade Classifier: Used for face detection.

Getting Started

Prerequisites

Ensure the following are installed:

  • Python 3.x
  • OpenCV (pip install opencv-python)
  • NumPy (pip install numpy)

Installation

  1. Clone the repository: 
    git clone https://github.com/nayanatara07/Face-Recognition-Based-Attendance-System-master.git
  2. Navigate to the project directory: 
    cd face-recognition-attendance

Usage

  1. Train the system using the provided training script:

    python train.py

    This generates a trainer.yml file in the training image label folder.

  2. Run the face recognition program to capture and log attendance.

  3. Attendance logs will be stored in the attendance folder with date and time.

Project Structure

face-recognition-attendance/
│
├── attendance/                # Folder containing attendance logs with date and time
├── student details/           # Contains student details CSV file
│   └── student_details.csv    # CSV file with registered user data
│
├── training image label/      # Folder storing the trained data
│   └── trainer.yml            # Trained data file for facial recognition
│
├── haarcascade_frontalface_default.xml  # Pre-trained Haar Cascade model
├── train.py                   # Script for training the facial recognition model
├── .gitignore                 # Git ignore file
└── README.md                  # Project documentation

Future Enhancements

  • Add a user-friendly GUI for better interaction.
  • Implement cloud-based storage for remote attendance access.
  • Improve recognition accuracy using deep learning algorithms.

Contributing

Contributions are welcome! Feel free to submit issues or pull requests for improvements.

Happy coding and efficient attendance tracking! 😊


Feel free to make further edits if needed!