A real-time, AI-powered smart attendance system built using Python and OpenCV. The application automatically detects student faces through a webcam feed and instantly marks their attendance for the lecture hour. This eliminates manual roll-calls and provides a fast, automated, and efficient workflow.
- 🎥 Real-time face detection using OpenCV
- 🧑🎓 Automatic student identification
- 📝 Instant attendance marking
- 📊 Dashboard for viewing attendance logs
- 📁 Data stored securely (CSV/Database)
- 🔄 Duplicate prevention for each session
- Python
- OpenCV
- NumPy
- Tkinter / Flask (depending on dashboard implementation)
- CSV / SQLite for data logging
/Smart-Attendance/
│── main.py
│── detect_and_mark.py
│── dashboard.py
│── models/ (face data)
│── dataset/ (student images)
│── attendance/ (logs)
│── requirements.txt
│── README.md
- Install dependencies:
pip install -r requirements.txt- Run the main application:
python main.py- The webcam captures the classroom feed.
- OpenCV detects and identifies student faces.
- Attendance is automatically marked with timestamps.
- The dashboard displays live and past records.
python, opencv, computer-vision, face-detection, face-recognition,
attendance-system, smart-attendance, automation, ai-project,
real-time-detection, image-processing, student-attendance