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This project detects and recognizes faces from a webcam feed, marks attendance with timestamps, and optionally triggers Arduino-based hardware (e.g., LED indicator)\n\n---\n\n## 🛠 Features\n\n- Real-time face detection and recognition\n- New user registration with duplicate face prevention\n- Attendance logging with date and time\n- GUI interface using Tkinter\n- View and manage attendance records\n- Delete user data and retrain model\n- Optional Arduino support for hardware triggers (e.g., LED blink)\n\n---\n\n## 📸 Technologies Used\n\n- Python 3.x\n- OpenCV (`opencv-contrib-python`)\n- Tkinter (standard GUI library)\n- Pillow (for image rendering)\n- Pandas (CSV handling)\n- Arduino (via `pyserial` for serial communication)\n\n---\n\n## 🧱 Directory Structure\n\nFace-Attendance-System/\n├── dataset/ # Captured face images\n├── trainer/ # Trained model file (trainer.yml)\n├── attendance.csv # Attendance records\n├── names.pkl # Pickled dictionary of user IDs and names\n├── user_id.pkl # Tracks next user ID\n├── haarcascade_frontalface_default.xml # Haar Cascade for face detection\n├── main.py # Main application script\n└── README.md # Project documentation\n\n👤 Adding a New User\n\u003eEnter the user's name in the text field.\n\n\u003eClick \"Add New User\".\n\n\u003eThe system will capture 30 face images and train the model.\n\n\u003eDuplicates are detected using confidence scores and live validation.\n\n📅 Viewing Attendance\n\u003eClick \"Show Attendance\" to view all records.\n\n\u003eYou can delete individual or all records from within the GUI.\n\n🗑 Deleting a User\n\u003eSelect a name from the dropdown and click \"Delete User\".\n\n\u003eTheir images and training data will be removed and the model will retrain automatically.\n\n📌 Notes\n\u003eFace data and user names are stored using pickle.\n\n\u003eAttendance is stored in attendance.csv.\n\n\u003eThe app locks repeated attendance marking for the same user within 30 seconds.\n\n📷 Screenshots\n\u003eAdd screenshots here showing:\n\n\u003eThe GUI\n\n\u003eReal-time face recognition\n\n\u003eAttendance window\n\n🧠 Future Improvements\n\u003eCloud database integration (Firebase / MongoDB)\n\n\u003eMask detection support\n\n\u003eRole-based access\n\n\u003eDeploy as a desktop app with PyInstaller\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnotorious0631%2Ffast-track","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnotorious0631%2Ffast-track","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnotorious0631%2Ffast-track/lists"}