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🎯 InstantAttend — Real-Time Face Recognition Attendance System

InstantAttend eliminates manual roll calls by automatically marking attendance using real-time face recognition — straight from your webcam, with a clean web dashboard.

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📸 Demo

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✨ Features

• 📷 Live Webcam Face Detection — Detects and identifies faces in real-time using OpenCV's Haar Cascade
• 🧠 KNN-based Face Recognition — Trains a K-Nearest Neighbours model on registered user faces
• 📋 Auto Attendance Logging — Marks attendance with name, ID, and timestamp into a daily CSV
• 🌐 Flask Web Dashboard — Clean browser UI to view today's attendance and manage users
• ➕ Easy User Registration — Capture 50 face samples per user and auto-retrain the model
• 📁 Daily CSV Reports — Attendance saved as Attendance-MM_DD_YY.csv for easy export

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🏗️ System Architecture

Webcam Input
    │
    ▼
Face Detection (Haar Cascade)
    │
    ├──► Preprocessing (Grayscale + Resize to 50×50)
    │
    ├──► Feature Extraction → KNN Model Training (on registration)
    │
    └──► Face Identification → Attendance Recording → CSV + Web UI

Data Architecture	Use Case Diagram

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🗂️ Project Structure

InstantAttend/
│
├── app.py
├── requirements.txt
├── README.md
├── .gitignore
├── screenshot.png
│
├── templates/
│   └── home.html
│
├── static/
│   ├── faces/
│   │   └── Name_ID/
│   └── face_recognition_model.pkl
│
├── Attendance/
│   └── Attendance-MM_DD_YY.csv
│
└── docs/
    ├── data_architecture.png
    └── use_case_diagram.png

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⚙️ How It Works

1. Register a New User

• Enter name + ID in the web form → click Add New User
• Webcam opens and captures 50 face images automatically
• Model retrains immediately with the new user included

2. Take Attendance

• Click Take Attendance → webcam opens
• System detects face → matches against trained KNN model
• Attendance marked with name, roll number, and timestamp
• Press ESC to close the webcam and view the updated table

3. View Records

• Dashboard shows today's attendance in real-time
• Raw CSV saved in /Attendance/ folder for easy export

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🚀 Getting Started

Prerequisites

• Python 3.8+
• A working webcam

Installation

git clone https://github.com/BiplabaKrSamal/InstantAttend.git
cd InstantAttend
pip install -r requirements.txt
python app.py

Open in Browser

http://127.0.0.1:5000

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📊 Technology Stack

Layer	Technology
Backend	Python, Flask
Face Detection	OpenCV (Haar Cascade Classifier)
Face Recognition	scikit-learn (KNN Classifier)
Frontend	HTML5, Bootstrap 5, Jinja2
Data Storage	CSV (Pandas), SQL

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🔮 Future Improvements

• Anti-spoofing / liveness detection
• Email daily report to admin
• Multi-camera support
• Cloud database integration (Firebase / PostgreSQL)
• Deep learning upgrade (FaceNet / DeepFace) for higher accuracy

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first.

1. Fork the repo
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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Made with ❤️ by BiplabaKrSamal | InstantAttend