https://github.com/notorious0631/fast-track

Face Detection Attendance System A real-time face recognition attendance system built using OpenCV, Tkinter, and Arduino. This project detects and recognizes faces from a webcam feed, marks attendance with timestamps, and optionally triggers Arduino-based hardware (e.g., LED indicator).
https://github.com/notorious0631/fast-track

face face-detection face-recognition opencv opencv-python python

Last synced: about 1 month ago
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Face Detection Attendance System A real-time face recognition attendance system built using OpenCV, Tkinter, and Arduino. This project detects and recognizes faces from a webcam feed, marks attendance with timestamps, and optionally triggers Arduino-based hardware (e.g., LED indicator).

• Host: GitHub
• URL: https://github.com/notorious0631/fast-track
• Owner: notorious0631
• Created: 2025-06-30T17:25:52.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-07-11T09:42:47.000Z (11 months ago)
• Last Synced: 2025-07-11T12:41:52.037Z (11 months ago)
• Topics: face, face-detection, face-recognition, opencv, opencv-python, python
• Language: Python
• Homepage:
• Size: 537 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Fast-Track.

# Face Detection Attendance System

A real-time face recognition attendance system built using **OpenCV**, **Tkinter**, and **Arduino**. This project detects and recognizes faces from a webcam feed, marks attendance with timestamps, and optionally triggers Arduino-based hardware (e.g., LED indicator)

---

## 🛠 Features

- Real-time face detection and recognition

- New user registration with duplicate face prevention

- Attendance logging with date and time

- GUI interface using Tkinter

- View and manage attendance records

- Delete user data and retrain model

- Optional Arduino support for hardware triggers (e.g., LED blink)

---

## 📸 Technologies Used

- Python 3.x

- OpenCV (`opencv-contrib-python`)

- Tkinter (standard GUI library)

- Pillow (for image rendering)

- Pandas (CSV handling)

- Arduino (via `pyserial` for serial communication)

---

## 🧱 Directory Structure

Face-Attendance-System/

├── dataset/ # Captured face images

├── trainer/ # Trained model file (trainer.yml)

├── attendance.csv # Attendance records

├── names.pkl # Pickled dictionary of user IDs and names

├── user_id.pkl # Tracks next user ID

├── haarcascade_frontalface_default.xml # Haar Cascade for face detection

├── main.py # Main application script

└── README.md # Project documentation

👤 Adding a New User

>Enter the user's name in the text field.

>Click "Add New User".

>The system will capture 30 face images and train the model.

>Duplicates are detected using confidence scores and live validation.

📅 Viewing Attendance

>Click "Show Attendance" to view all records.

>You can delete individual or all records from within the GUI.

🗑 Deleting a User

>Select a name from the dropdown and click "Delete User".

>Their images and training data will be removed and the model will retrain automatically.

📌 Notes

>Face data and user names are stored using pickle.

>Attendance is stored in attendance.csv.

>The app locks repeated attendance marking for the same user within 30 seconds.

📷 Screenshots

>Add screenshots here showing:

>The GUI

>Real-time face recognition

>Attendance window

🧠 Future Improvements

>Cloud database integration (Firebase / MongoDB)

>Mask detection support

>Role-based access

>Deploy as a desktop app with PyInstaller