Real-time face recognition that logs who's present — no badges, no swipes, no headaches.
📸 Screenshot / GIF of the system in action goes here
FaceTrack v2 replaces manual attendance tracking with a live computer-vision pipeline. Point a camera at a room — it identifies faces, matches them to registered users, and writes a timestamped CSV record. All of this happens through a clean web UI with zero command-line friction for end users.
| Feature | What You Get |
|---|---|
| ⚡ Real-Time Face Detection | Instant detection via Haar Cascades — no lag, no fuss |
| 🎯 KNN Face Recognition | Accurate identity matching from a trained K-Nearest Neighbors model |
| 📋 Attendance Logging | Name, roll number, and timestamp saved to a daily CSV automatically |
| 🌐 Web Interface | Add/manage users through a simple Flask UI — no CLI needed |
| 🔄 Auto Model Retraining | Recognition model updates itself every time a new user is added |
┌─────────────────────────────────────────────────────────┐
│ CAMERA FEED │
└──────────────────────────┬──────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ FACE DETECTION (Haar Cascades) │
│ Scans each frame and crops detected face regions │
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ FACE RECOGNITION (KNN Classifier) │
│ Compares face embeddings against registered users │
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ ATTENDANCE LOGGING (CSV via pandas) │
│ Writes name + roll number + timestamp to daily file │
└──────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ WEB UI (Flask + HTML Templates) │
│ View attendance, manage users, trigger retraining │
└──────────────────────────────────────────────────────────┘
New user added? → Face images are captured → rebuild_model.py retrains the KNN → system is immediately updated. No manual steps required.
git clone https://github.com/ragibcs/face-recognition-attendance-system-v2.git
cd face-recognition-attendance-system-v2
pip install -r requirements.txtpython app.pyhttp://localhost:5000
That's it. Three steps and you're live.
face-recognition-attendance-system-v2/
│
├── app.py # 🚀 Main Flask app — start here
├── rebuild_model.py # 🔄 Retrains the KNN recognition model
├── requirements.txt # 📦 All dependencies
├── haarcascade_frontalface_default.xml # 🔍 Haar Cascade for face detection
│
├── static/
│ ├── faces/ # 🗃️ Stored face images per user
│ └── face_recognition_model.pkl # 🧠 Serialized trained model
│
├── templates/ # 🌐 HTML pages
│ ├── home.html
│ ├── add_user.html
│ └── attendance.html
│
├── Attendance/ # 📋 Daily attendance CSVs
└── Images/ # 🖼️ Project assets
Here's what's coming next:
- 🔒 Anti-spoofing — detect printed photos / screen attacks
- 📊 Analytics dashboard — attendance trends and statistics
- 📧 Email/SMS alerts — notify when a user is marked present or absent
- 🗄️ Database backend — replace CSV storage with SQLite/PostgreSQL
- 📱 Mobile-friendly UI — responsive redesign for tablets and phones
- 🐳 Docker support — one-command deployment
Have an idea? Open an issue and let's talk.
Contributions are welcome and appreciated. Here's the flow:
- Fork the repository
- Create a feature branch:
git checkout -b feature/your-idea - Commit your changes:
git commit -m "Add: your feature" - Push to your branch:
git push origin feature/your-idea - Open a Pull Request — describe what you built and why
Please keep PRs focused (one feature or fix per PR) and make sure existing functionality still works before submitting.
Distributed under the MIT License — use it, fork it, ship it. See LICENSE for details.
Made with ❤️ by ragibcs
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