This project is a Python-based Face Recognition Attendance Tracker designed to automate attendance management using computer vision. By leveraging OpenCV, NumPy, and the face_recognition library, the system detects and recognizes faces in real-time and logs attendance into a database.
The solution eliminates manual roll calls, improves accuracy, and provides a scalable approach for classrooms, offices, and events.
- Real-time face detection and recognition using webcam
- Automatic attendance logging with name, date, and time
- Integration with CSV/SQLite database
- Duplicate entry prevention
- Simple GUI displaying live feed and attendance status
- Modular and extensible Python codebase
- Programming Language: Python
- Libraries: OpenCV, NumPy, face_recognition, SQLite3, Pandas
- Database: CSV / SQLite
- Tools: Jupyter Notebook / VS Code
- Data Collection – Capture and store facial images of individuals.
- Face Encoding – Convert facial features into numerical vectors.
- Recognition – Compare live camera input against stored encodings.
- Attendance Logging – Mark attendance automatically in CSV/SQLite.
- Visualization – Display live feed with recognition status.
├── dataset/ # Stored facial images ├── attendance.csv # Attendance log file ├── main.py # Core recognition and logging script ├── requirements.txt # Dependencies └── README.md # Project documentation
- Python scripting & workflow optimization
- Computer Vision (OpenCV, HOG, CNN models)
- Database integration & automation
- Error handling & robustness improvements
- GUI design for user-friendly interaction
- Cloud-based attendance storage
- Mobile app integration
- Multi-camera support
- Role-based access control