🎯 Deep Learning Face Recognition Attendance System

An advanced, intelligent, real-time facial recognition attendance system built with state-of-the-art Deep Learning models. This system automatically detects faces, matches them against registered students with high precision, and effortlessly logs subject-wise and date-wise attendance.

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✨ Features

• 🧠 Deep Learning Core: Powered by PyTorch, utilizing MTCNN for robust face detection and FaceNet (InceptionResnetV1) for generating high-accuracy facial embeddings.
• ⚡ Fast & Async Backend: Built on FastAPI for blisteringly fast API responses and asynchronous processing.
• 🎥 Real-Time Monitoring: Process camera feeds locally using OpenCV with a temporal stability tracker that prevents duplicate marks and false positives.
• 📊 Interactive Admin Dashboard: A sleek, glassmorphic UI for tracking active sessions, registering students, viewing attendance history, and filtering records by date and subject.
• 🗄️ Dual Database Support: Seamlessly switch between local SQLite for rapid testing or cloud-based Firebase Firestore for production deployments.
• 📄 Extensible Exports: Instantly download professional attendance reports in Excel (.xlsx) and PDF formats directly from the dashboard.
• 🐳 Docker Ready: Easily containerized for cloud deployment (Azure, AWS, GCP) using the provided Dockerfile.

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🛠️ Technology Stack

Component	Technology Used
Backend API	Python, FastAPI, Uvicorn
AI Models	PyTorch, facenet-pytorch (MTCNN / InceptionResnetV1), OpenCV
Databases	Firebase Admin SDK (Firestore), SQLite3
Data Export	Pandas, OpenPyXL (Excel), FPDF (PDF)
Frontend UI	HTML5, CSS3, Vanilla JavaScript, Jinja2 Templates
Deployment	Docker

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🚀 Getting Started (Local Development)

1. Prerequisites

• Python 3.10+
• Git
• A webcam (for registering students and scanning attendance).

2. Installation Setup

Clone the repository and install the dependencies within a virtual environment.

git clone https://github.com/your-username/face-attendance-system.git
cd face-attendance-system

# Create and activate virtual environment
python -m venv venv
# On Windows:
venv\Scripts\activate
# On Linux/Mac:
source venv/bin/activate

# Install requirements
pip install -r requirements.txt

3. Environment Variables

Create a .env file in the root directory (use .env.example as a template).

DATABASE_MODE=sqlite # Change to 'firebase' for cloud syncing
ADMIN_USERNAME=admin
ADMIN_PASSWORD=password
SESSION_SECRET_KEY=generate_a_secure_random_string

# Only required if using Firebase
FIREBASE_CREDENTIALS_PATH=firebase.json 

4. Run the Server

Start the FastAPI server locally:

uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload

Open your browser and navigate to http://localhost:8000 to access the login page and admin dashboard.

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☁️ Deployment (Microsoft Azure Web App Container)

This application can easily be deployed on Azure Student Tier using Docker.

1. Create an Azure Container Registry (ACR) inside the Azure Portal.
2. Link your GitHub repository to Azure App Services.
3. Select Linux Container and choose the Basic B1 pricing tier (Requires at least 1.75GB RAM to safely load PyTorch).
4. Do not commit your .env or firebase.json text files. Input your environment variables securely directly inside the Azure App Service Configuration menu.

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🤝 Contributing

Contributions, issues, and feature requests are always welcome! Feel free to check the issues page.

📝 License

This project is licensed under the MIT License.