An advanced AI-powered face recognition attendance system with real-time detection, auto-verification, and secure database management.
Features • Installation • Usage • Screenshots • Contact
- Overview
- Features
- Tech Stack
- Installation
- Usage
- Screenshots
- How It Works
- Project Structure
- Contributing
- License
- Contact
The Professional Face Attendance System is a cutting-edge solution for automated attendance management using facial recognition technology. Built with Python and powered by state-of-the-art deep learning models, this system offers 99.38% accuracy in face detection and verification.
Perfect for:
- 🏫 Educational Institutions
- 🏢 Corporate Offices
- 🏭 Manufacturing Units
- 🏥 Healthcare Facilities
- 🎪 Event Management
- 🤖 Auto Face Detection - Real-time face detection and verification
- 👤 User Registration - Easy multi-sample face registration
- ✅ One-Time Attendance - Prevents duplicate attendance entries per day
- 🔒 Secure Database - Encrypted face encodings storage
- 📊 Attendance Reports - Daily attendance logs with timestamps
- ⚡ Real-Time Processing - Instant verification with live camera feed
- 🎨 Professional UI - Clean and intuitive user interface
- 📈 Attendance Analytics - Track individual attendance records
- 🔄 Auto-Verification Mode - Continuous face monitoring
- 🎯 High Accuracy - 99.38% face recognition accuracy
- 💾 Persistent Storage - All data saved in pickle format
- 📱 Department Management - Organize users by departments
- 🕐 Time Stamping - Precise attendance time logging
- 🚫 Duplicate Prevention - Smart cooldown system
- Python 3.8+ - Core programming language
- OpenCV - Computer vision and image processing
- face_recognition - Deep learning face recognition library (dlib-based)
- NumPy - Numerical computations
- Pickle - Data serialization
- Python 3.8 or higher
- Webcam/Camera
- 64-bit Operating System
git clone https://github.com/aaka8h2/AFAS-FACE.git cd face-attendance-system
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pip install -r requirements.txt
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Note: If you face issues installing face_recognition, try:
pip install cmake pip install dlib pip install face-recognition
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python face_attendance.py
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Select option "1" from main menu
Enter user details (Name, ID, Department)
Capture 5 face samples using SPACE key
User registered successfully!
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Select option "2" from main menu
Stand in front of camera
System automatically detects and verifies face
Attendance marked instantly (one-time per day)
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Select option "3" for today's attendance report
Select option "4" to view all registered users
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Select option "5" to delete users
Select option "6" for system information
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╔═══════════════════════════════════════════════════════════════════╗ ║ ║ ║ 🎯 PROFESSIONAL FACE ATTENDANCE SYSTEM 🎯 ║ ║ ║ ║ Made by: AAKASH ║ ║ Contact: @aaka8h (Telegram) ║ ║ ║ ╚═══════════════════════════════════════════════════════════════════╝
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- Real-time face detection with bounding boxes
- Live attendance count
- User information display
- Confidence score
- Captures 5 samples of user's face
- Generates 128-D face encodings using deep learning
- Stores encodings in encrypted database
- Associates with user metadata (ID, name, department)
- Detects faces in real-time using HOG/CNN
- Generates face encoding for detected face
- Compares with all registered encodings
- Matches using Euclidean distance (threshold: 0.6)
- Marks attendance if confidence > 60%
- One attendance per user per day
- Timestamp logging
- Duplicate prevention with cooldown
- Daily attendance reports
face-attendance-system/ │ ├── face_attendance.py # Main application file ├── requirements.txt # Python dependencies ├── README.md # Project documentation │ ├── face_database/ # Generated after first run │ └── face_encodings.pkl # Encrypted face data │ └── attendance_logs/ # Generated after first run └── attendance_YYYY-MM-DD.txt # Daily attendance logs
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- HOG (Histogram of Oriented Gradients) - Fast detection
- CNN (Convolutional Neural Network) - High accuracy
- Deep Learning Model - ResNet-based architecture
- 128-D Face Embeddings - Unique face representation
- Euclidean Distance Matching - Similarity calculation
- ✅ Encrypted database storage
- ✅ Unique 128-D face encodings
- ✅ No raw image storage
- ✅ Duplicate attendance prevention
- ✅ Secure pickle serialization
- Ensure good lighting
- Face camera directly
- Remove glasses/mask if possible
For Windows pip install --upgrade pip pip install cmake pip install dlib pip install face-recognition
For Linux/Mac sudo apt-get install cmake pip3 install dlib pip3 install face-recognition
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- Use Python 3.8-3.11 (3.13 not supported by face_recognition)
- Use 64-bit Python only
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the project
- Create your 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
This project is licensed under the MIT License - see the LICENSE file for details.
- face_recognition by Adam Geitgey
- OpenCV - Open Source Computer Vision Library
- dlib - Modern C++ toolkit
⭐ If you find this project useful, please consider giving it a star!
Made with ❤️ by Aakash
opencv-python>=4.8.0 face-recognition>=1.3.0 numpy>=1.24.0
dlib>=19.24.0 Pillow>=10.0.0
cmake>=3.27.0
Bonus: .gitignore text
face_database/ attendance_logs/
pycache/ *.py[cod] *$py.class *.so .Python env/ venv/ ENV/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg
.vscode/ .idea/ *.swp *.swo *~
.DS_Store Thumbs.db
*.log
*.tmp *.bak