Face Recognition with Real-Time Database

Introduction

This project is a real-time Face Attendance System that uses face recognition and a Firebase real-time database to track attendance efficiently. The system captures live video, detects faces, and marks attendance automatically in the database.

Features

✅ Real-time face detection using OpenCV
✅ Face recognition using pre-stored encodings
✅ Live database integration with Firebase
✅ Graphical user interface for better interaction
✅ Automatic attendance marking with duplicate prevention
✅ Image storage in Firebase Storage

Technology Stack

• Python
• OpenCV (cv2) – Video processing
• face_recognition – Face detection and encoding
• Firebase – Real-time database & storage
• cvzone – UI enhancements
• pickle – Data serialization
• NumPy – Data handling

Project Workflow

1. Setup Firebase and add credentials (serviceAccountKey.json)
2. Run AddDatatoDatabase.py to add student data to Firebase
3. Run EncodeGenerator.py to generate face encodings
4. Run Main.py to start the real-time face recognition system
5. System detects and identifies faces
6. Attendance is updated in Firebase

File Structure

Face-Attendance-System/
│-- Resources/
│   │-- background.png  # UI background
│   │-- Modes/  # Mode images
│-- Images/  # Student images for encoding
│-- EncodeFile.p  # Encoded face data
│-- serviceAccountKey.json  # Firebase credentials
│-- Main.py  # Main execution file
│-- EncodeGenerator.py  # Generates face encodings
│-- AddDatatoDatabase.py  # Adds student records to Firebase
│-- README.md  # Project documentation

Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/codewithdhruba01/FaceRecognitionRealTime.git
cd Face-Recognition-Attendance

2️⃣ Install Dependencies

pip install opencv-python numpy face-recognition firebase-admin cvzone

3️⃣ Setup Firebase

• Create a Firebase project at Firebase Console
• Enable Realtime Database and Storage
• Download the serviceAccountKey.json file and place it in the project directory

4️⃣ Run the Scripts

Step 1: Add Student Data to Firebase

python AddDatatoDatabase.py

Step 2: Generate Face Encodings

python EncodeGenerator.py

Step 3: Start Face Recognition System

python Main.py

System Flowchart

graph TD;
    A[Start] --> B[Load Face Encodings]
    B --> C[Capture Webcam Feed]
    C --> D[Detect Faces]
    D -->|Match Found| E[Fetch Data from Firebase]
    D -->|No Match| G[Unknown Face - No Action]
    E --> F[Check Last Attendance Time]
    F -->|Already Marked| H[Skip Attendance]
    F -->|Not Marked| I[Update Attendance in Firebase]
    I --> J[Display Student Info on UI]
    J --> K[End Process]
    G --> K
    H --> K

Future Enhancements

🔹 Multi-camera support for better scalability
🔹 Mobile app integration for easier attendance tracking
🔹 Cloud-based AI models for more accurate face recognition

License

This project is open-source and available under the MIT License.

Contributors

👤 Dhrubaraj Pati - GitHub Profile

Acknowledgments

Special thanks to OpenCV, Firebase, and Face Recognition communities for making this project possible!