🎯 Face Detection Attendance System

A modern, real-time face recognition attendance system built with Python Flask and advanced computer vision technology. This system automatically detects and recognizes registered faces to mark attendance, providing a seamless and contactless attendance tracking solution.

📸 User Interface Screenshots

Main Dashboard

Registration Interface with File Upload

The registration interface now supports both camera capture and direct file upload for maximum flexibility.

✨ Features

Real-time Face Detection with live camera feed
High Accuracy using face_recognition (dlib)
Multiple Registration Methods: Camera capture or direct file upload
Automatic Attendance Marking
Duplicate Prevention: 10-second cooldown
Daily Tracking: Separate records for each day
Live Notifications and WebSocket updates
Modern, Responsive UI (Bootstrap 5)
Secure File Handling and validation
Automatic browser launch on app start

🛠️ Technology Stack

Backend: Python Flask
Face Recognition: face_recognition library
Real-time Communication: Flask-SocketIO with Eventlet
Frontend: HTML5, CSS3, JavaScript
UI Framework: Bootstrap 5
Image Processing: Pillow (PIL)
Data Storage: JSON files

📋 Prerequisites

Python 3.7+
pip (Python package manager)
Webcam or camera device
Modern web browser (Chrome, Edge, Firefox, etc.)
Internet connection for dependencies

🚀 Installation

Step 1: Clone the Repository

git clone <repository-url>
cd SmartAttendance-FaceID

Step 2: Install Dependencies

For Windows

Install Visual Studio Build Tools
- During installation, select "Desktop development with C++".
Install CMake
Or via pip:
```
pip install cmake
```

Install Python dependencies

pip install --upgrade pip
pip install -r requirements.txt
pip install dlib
pip install face_recognition

For macOS

brew install cmake
pip install --upgrade pip
pip install -r requirements.txt
pip install dlib
pip install face_recognition

For Ubuntu/Debian

sudo apt-get update
sudo apt-get install cmake
sudo apt-get install libopenblas-dev liblapack-dev
sudo apt-get install libx11-dev libgtk-3-dev
sudo apt-get install libboost-python-dev
pip install --upgrade pip
pip install -r requirements.txt
pip install dlib
pip install face_recognition

Step 3: Run the Application

python app.py

The application will start on http://localhost:5000
Your browser will open automatically!

Step 4: Test the Application (Optional)

python test_file_upload.py   # Test file upload functionality
python test_attendance.py    # Test attendance API

📖 Usage Guide

1. Starting the System

Run python app.py and your browser will open automatically
Click "Start Camera" to initialize the webcam
Ensure camera permissions are granted

2. Registering New Faces

Method 1: Camera Capture

Click "Start Camera"
Click "Capture Photo"
Enter the person's name
Click "Register Face"

Method 2: File Upload

Click "Choose File"
Select an image (PNG, JPG, JPEG, max 5MB)
Preview the image
Enter the person's name
Click "Register Face"
Use the "Clear" button to remove uploaded files if needed

💡 Pro Tip: You can switch between camera and file upload seamlessly.

3. Starting Face Detection

Click "Start Detection" to begin real-time face recognition
Attendance is marked automatically

4. Monitoring Attendance

View real-time attendance in the "Today's Attendance" panel
Check "Live Detections" for instant notifications
Use "Refresh" buttons to update lists

5. System Status

Connection: WebSocket status
Camera: Camera feed status
Detection: Face detection status
Registered Faces: Count of registered faces

📁 Project Structure

SmartAttendance-FaceID/
├── app.py                      # Main Flask application
├── requirements.txt            # Python dependencies
├── registered_faces.json       # Stored face encodings
├── attendance.json             # Attendance records
├── UI.png                      # Main dashboard screenshot
├── UI-of-Register.png          # Registration interface screenshot
├── test_file_upload.py         # File upload functionality test
├── test_attendance.py          # Attendance API test
├── static/
│   ├── css/
│   │   └── style.css          # Custom styles
│   └── js/
│       └── app.js             # Frontend JavaScript
├── templates/
│   └── index.html             # Main HTML template
├── uploads/                   # Stored face images
└── README.md                  # This file

🔧 Configuration

Tolerance: Adjustable in app.py (default: 0.5)
Cooldown Period: 10 seconds between attendance marks
Image Formats: PNG, JPG, JPEG
File Size Limit: 5MB per image
Face Detection: Requires at least one clear face in the image
Face Images: Stored in uploads/
Data Files: JSON format

🔒 Security Features

File Validation: Only allowed image formats accepted
Secure Filenames: Automatic filename sanitization
Input Validation: Server-side validation for all inputs
Error Handling: Graceful error handling and user feedback

🆕 Recent Updates

Direct Image Upload: Upload images directly from your device
File Validation: Automatic validation of image formats and sizes
Preview Functionality: See uploaded images before registration
Clear Button: Easy removal of uploaded files
Dual Method Support: Seamless switching between camera and file upload
Enhanced UI: Modern file upload interface
Error Handling: Comprehensive error messages for invalid files
Automatic browser launch: Opens your browser when you run the app

🐛 Troubleshooting

Face Recognition/Dlib Installation Issues (Windows)

CMake or compiler errors?
- Make sure you have installed both CMake and Visual Studio Build Tools (with C++ support).
- Restart your terminal after installation.
- Check that cmake --version works in your terminal.
Still having issues?
- Try installing pre-built wheels from https://www.lfd.uci.edu/~gohlke/pythonlibs/#dlib (advanced users).
General tips:
- Use a virtual environment:
```
python -m venv venv
venv\Scripts\activate  # On Windows
source venv/bin/activate  # On macOS/Linux
```
- Upgrade pip: python -m pip install --upgrade pip
- If you see errors about numpy or Pillow, run pip install numpy Pillow before installing dlib or face_recognition.

Other Common Issues

Camera Not Working
- Ensure camera permissions are granted
- Check if camera is being used by another application
- Try refreshing the page
Face Recognition Not Working
- Ensure good lighting conditions
- Face should be clearly visible and centered
- Check if face is properly registered
Performance Issues
- Close other applications using the camera
- Ensure adequate system resources
- Consider reducing camera resolution

📊 API Endpoints

Registration

POST /api/register - Register a new face (supports both file upload and base64 image)
- File Upload: Send multipart/form-data with file and name fields
- Camera Capture: Send JSON with image (base64) and name fields
GET /api/registered-faces - Get list of registered faces

Attendance

POST /api/attendance - Mark attendance manually
GET /api/attendance-list - Get today's attendance

File Access

GET /uploads/<filename> - Access stored images

WebSocket Events

stream_frame - Send camera frame for real-time detection
face_detected - Receive detection results

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📝 License

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

👨‍💻 Author

Vignesh B S - Developer and Maintainer

🙏 Acknowledgments

face_recognition library by Adam Geitgey
Flask web framework
Bootstrap for responsive UI components
Socket.IO for real-time communication

📞 Support

If you encounter any issues or have questions:

Check the troubleshooting section above
Review the project documentation
Open an issue on the repository
Contact the development team