Realtime Face Attendance System

A highly efficient and accurate real-time face attendance system leveraging state-of-the-art computer vision and machine learning technologies.Seamless and automated attendance tracking using facial recognition

Table of Contents

• Introduction
• Features
• Architecture
• Demo
• Technologies Used
• Installation
• Usage
• Dataset
• Model Training
• Examples
• Contributing
• License
• Contact
• Acknowledgements

Introduction

Attendance management is a critical component in educational institutions and organizations. Real-time Face Attendance provides an automated solution to streamline the attendance process using advanced facial recognition technology. This system ensures accuracy, reduces manual effort, and enhances security by preventing proxy attendance.

Features

Real-time Face Recognition

Instantly recognize and record attendance as individuals enter the monitored area using live camera feeds.

High Accuracy

Employs deep learning models trained on extensive datasets to ensure precise face recognition even in varying lighting and angles.

Multi-User Support

Capable of handling multiple users simultaneously, making it ideal for large classrooms or auditoriums.

User-Friendly Interface

Intuitive dashboard for easy management and monitoring of attendance records.

Secure Data Handling

Ensures all attendance data is securely stored and processed, adhering to privacy standards.

Automated Report Generation

Generates comprehensive attendance reports with customizable parameters for analysis and record-keeping.

Architecture

System architecture showcasing the flow from image capture to attendance recording.

Image Capture: Utilizes webcams or IP cameras to capture live video streams.
Face Detection: Processes frames using OpenCV to detect faces in real-time.
Face Recognition: Applies a pre-trained deep learning model to identify individuals.
Attendance Logging: Records recognized faces with timestamps in the database.
User Interface: Displays real-time attendance status and provides administrative controls.

Deepface Architecture by Facebook

Demo

Experience the Real-time Face Attendance system in action! Demo Screenshot

Technologies Used

Python: 3.8+

OpenCV: For real-time image and video processing.

TensorFlow & Keras: For building and deploying deep learning models.

dlib: For robust face detection and landmark recognition.

SQLite/MySQL: For managing attendance databases.

Tkinter: For creating a seamless desktop-based user interface.

NumPy & Pandas: For data manipulation and analysis.

Git & GitHub: For version control and collaboration.

Installation

Prerequisites

• Python 3.8 or higher
• Git
• Virtual Environment Tool (e.g., venv, conda)
• Webcam or IP Camera for real-time video capture

Steps

1. Clone the Repository

   git clone https://github.com/yxshee/realtime-face-attendance.git
   cd realtime-face-attendance

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Download Pre-trained Models

   • Ensure that the models/ directory contains the necessary pre-trained models. If not, follow the Model Training section.

Usage

Running the Application

1. Activate the Virtual Environment

   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Launch the Application

   python app.py

3. Access the Interface

   • Open your browser and navigate to http://localhost:5000 to use the Real-time Face Attendance system.

Using the Attendance System

1. Register Users

   • Add new users by uploading their facial images or capturing them via the camera.
   • Assign unique identifiers (e.g., student ID, employee ID) to each user.

2. Start Attendance

   • Click on the "Start Attendance" button to begin real-time face recognition.
   • The system will automatically detect and recognize faces, logging attendance in the database.

3. View Attendance Records

   • Access the dashboard to view real-time attendance status and generate reports.

4. Generate Reports

   • Export attendance data in various formats (e.g., CSV, PDF) for analysis and record-keeping.

Dataset

The Real-time Face Attendance system requires a dataset of user facial images for accurate recognition. You can create your own dataset by registering users through the application interface.

Dataset Features

• Diverse Users: Supports multiple users with unique identifiers.
• Varied Conditions: Captures images under different lighting, angles, and expressions to enhance model robustness.
• Secure Storage: Ensures all facial data is securely stored and encrypted in the database.

Note: For privacy and security reasons, the dataset is not publicly available. Ensure compliance with data protection regulations when collecting and storing facial data.

Model Training

If you need to train the face recognition model from scratch or update it with new data, follow these steps:

1. Prepare the Dataset

Organize user facial images into the following directory structure:

data/
├── train/
│   ├── user1/
│   ├── user2/
│   └── userN/
├── validation/
│   ├── user1/
│   ├── user2/
│   └── userN/

2. Data Augmentation (Optional)

Enhance the dataset with augmented images to improve model robustness.

python data_augmentation.py --input_dir data/train/ --output_dir data/augmented_train/

3. Train the Model

python train_model.py --data_dir data/train/ --model_dir models/

4. Evaluate Performance

Assess the model's accuracy, precision, recall, and F1-score.

python evaluate_model.py --model_dir models/

5. Deploy the Model

Ensure the trained model files are placed in the models/ directory for the application to use.

Examples

User Registration

Registering a new user by capturing their facial images.

Real-time Attendance

System recognizing and logging attendance in real-time.

Attendance Reports

Comprehensive attendance reports generated by the system.

Contributing

Contributions are welcome! Whether it's reporting bugs, suggesting features, or submitting pull requests, your input helps improve the Real-time Face Attendance system.

How to Contribute

1. Fork the Repository

   • Click the "Fork" button at the top-right corner of this page.

2. Clone Your Fork

   git clone https://github.com/your-username/realtime-face-attendance.git
   cd realtime-face-attendance

3. Create a Feature Branch

   git checkout -b feature/YourFeature

4. Commit Your Changes

   git commit -m "Add your feature"

5. Push to the Branch

   git push origin feature/YourFeature

6. Open a Pull Request

   • Navigate to the original repository and open a pull request from your fork.

Contribution Guidelines

• Code Quality: Ensure your code follows the project's coding standards and is well-documented.
• Testing: Include relevant tests for new features or bug fixes.
• Issue Tracking: Before working on a new feature or bug, check existing issues to avoid duplicates.
• Respect and Collaboration: Be respectful and considerate in all interactions. Collaborate effectively with other contributors.

License

This project is licensed under the MIT License.

Contact

For any inquiries, issues, or contributions, please contact:

• Author: Yash Dogra
• Email: [email protected]

Feel free to open an issue or reach out directly for collaboration opportunities!

Acknowledgements

• OpenCV: For providing powerful computer vision tools.
• TensorFlow & Keras: For enabling efficient deep learning model development.
• dlib: For robust face detection and landmark recognition.
• Flask/Django: For creating a seamless web-based user interface.
• Bootstrap: For responsive and modern UI design.
• Community Contributors: Special thanks to all contributors and supporters who helped make this project possible.

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