Smart Attendance System Using Face Recognition

Overview

This project implements a smart attendance system using face recognition technology, allowing efficient and automated attendance tracking for educational institutions. The system utilizes various libraries for face detection and recognition.

Features

• Real-time face recognition: Automatically identifies students from the video stream.
• Attendance logging: Maintains records of student attendance.
• User-friendly interface: Easy for instructors and administrators to use.

Technologies Used

• Python: Core programming language
• OpenCV: Computer vision library for face detection and image processing
• Haar Cascade Classifier: Pre-trained model for face detection (haarcascade_frontalface_default.xml)
• SQLite: Database for storing attendance records and face data
• NumPy: Numerical computing library

Installation

Prerequisites

• Python 3.6 or higher
• pip (Python package manager)
• Webcam or camera device

Steps

1. Clone the repository:

   git clone https://github.com/Adit-Jana/smart-attendance-system-using-face-recognition.git
   cd smart-attendance-system-using-face-recognition

2. Install required dependencies:

   pip install opencv-python opencv-contrib-python numpy

Usage

Step 1: Capture Training Dataset

Create a dataset of face images for the system:

python dataset1.py

This will capture multiple images of a person's face for training purposes.

Step 2: Train the Model

Train the face recognition model using the captured dataset:

python train2.py

This generates a trained recognizer model in the recognizer/ directory.

Step 3: Run Face Detection & Recognition

Execute the main application:

python final4.py

The system will:

• Detect faces in real-time using the webcam
• Recognize known faces and match them against the training data
• Log attendance records to attn.txt

Step 4: Verify Results

Check the attendance log:

cat attn.txt

Key Files Description

File	Purpose
dataset1.py	Captures face images and stores them for training
train2.py	Trains the face recognition model using captured images
detector3.py	Handles face detection using Haar Cascade classifier
drycheck.py	Validates the system and checks configurations
final4.py	Main application - runs the attendance system
FaceBase.db	SQLite database storing face encodings and user data
attn.txt	Text file logging attendance records with timestamps
haarcascade_frontalface_default.xml	Pre-trained classifier for face detection

How It Works

1. Face Detection: The system uses Haar Cascade Classifier to detect faces in the video stream
2. Face Training: Captured face images are processed and a recognizer model is trained
3. Face Recognition: During runtime, detected faces are compared against the trained model
4. Attendance Logging: Recognized faces are logged with timestamps in the attendance file

Configuration

• Modify dataset1.py to adjust the number of training images captured per person
• Adjust confidence thresholds in final4.py to control recognition accuracy
• Update database path in scripts if needed

Troubleshooting

Issue	Solution
Camera not detected	Check camera permissions and ensure the device is connected
Poor recognition accuracy	Capture more images in dataset1.py and retrain the model
Database errors	Ensure FaceBase.db has proper read/write permissions
Face not detected	Ensure adequate lighting and adjust Haar Cascade parameters

Future Enhancements

• Add support for multiple face recognition algorithms (LBPH, Deep Learning)
• Implement web interface for easy attendance viewing
• Add email notifications for administrators
• Support for integration with existing attendance systems
• Real-time dashboard for attendance statistics

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add YourFeature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenCV documentation and community
• Haar Cascade Classifier developers
• Contributors and users of this project

Contact

For further inquiries or support, please contact:

• Author: Adit Jana
• GitHub: Adit-Jana
• Repository: smart-attendance-system-using-face-recognition