README.md

NetraID - Production-Grade AI Face Authentication Attendance System

NetraID is an enterprise-grade, 100% free, and self-hosted face-authentication attendance platform. Using advanced facial biometrics, liveness classifiers, and high-performance vector indexes, it provides an offline, secure, and production-ready system suitable for digital kiosks, schools, colleges, and startups.

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Technical Stack & Architecture

NetraID uses clean-architecture layers (Repository and Service layers) to structure code modularly.

• AI & Computer Vision: SCRFD (Face Detection), ArcFace (Face Recognition), MiniFASNet (Silent Face Anti-Spoofing) running on pure ONNX Runtime.
• Backend API: Python 3.12, FastAPI, SQLAlchemy 2.0, Alembic, Pydantic V2, JWT RBAC, and Audit Logging.
• Voice synthesis: Offline pyttsx3 text-to-speech engine.
• Vector Database: PostgreSQL 16 with the native pgvector extension.
• Frontend Kiosk & Dashboard: Next.js 15, TypeScript, Tailwind CSS, Framer Motion, and Apache ECharts.
• Deployment: Docker, Docker Compose, and Nginx.

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Project Structure

NetraID/
├── backend/
│   ├── app/
│   │   ├── api/             # API Router endpoints
│   │   ├── core/            # Database connection, security, seeding
│   │   ├── crud/            # CRUD repositories (SQLAlchemy 2.0)
│   │   ├── models/          # Database ORM models
│   │   ├── schemas/         # Pydantic V2 validation schemas
│   │   └── services/        # FaceEngine AI & pyttsx3 TTS helper
│   ├── requirements.txt     # Python requirements
│   └── Dockerfile           # Backend image builder
├── frontend/
│   ├── app/                 # Next.js pages (Login, Dashboard, Kiosk)
│   ├── components/          # Shared components (SidebarLayout)
│   ├── package.json         # Node requirements
│   └── Dockerfile           # Frontend image builder
├── docker/
│   └── nginx.conf           # Reverse proxy configuration
├── docker-compose.yml       # Docker orchestrator
└── README.md                # System documentation

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Setup Instructions

NetraID runs natively on both Windows and Linux.

Option A: Quick Launch via Docker Compose (Recommended)

1. Clone & Navigate:

   cd NetraID
   

2. Download AI Model Weights: Run the model downloader script locally to pull the ONNX models to the ./models folder, or let the backend do it on boot. To run it manually:

   cd backend
   python -m pip install -r requirements.txt
   python app/core/download_models.py
   cd ..
   

3. Start Stack:

   docker compose up --build
   

4. Access Applications:

   • Dashboard & Kiosk: http://localhost (Nginx proxy)
   • FastAPI API Swagger Docs: http://localhost/docs
   • PostgreSQL Database: Port 5432

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Option B: Local Development Setup (Windows & Linux)

1. Setup PostgreSQL + pgvector

Make sure you have a running PostgreSQL instance and install the pgvector extension:

CREATE EXTENSION IF NOT EXISTS vector;

2. Run Backend

1. Initialize Virtual Env:

   cd backend
   python -m venv venv
   # On Windows:
   venv\Scripts\activate
   # On Linux:
   source venv/bin/activate
   

2. Install Packages:
   • On Linux: Install espeak for pyttsx3 and Mesa GL for OpenCV:

     sudo apt-get install -y libgl1-mesa-glx libglib2.0-0 espeak
     

   • On Windows: Python packages will install SAPI5 dependencies automatically.

   pip install -r requirements.txt
   

3. Download ONNX Models:

   python app/core/download_models.py
   

4. Boot Server:

   uvicorn app.main:app --reload --port 8000
   

3. Run Frontend

1. Navigate & Install:

   cd ../frontend
   npm install
   

2. Boot Dev Server:

   npm run dev
   

   Open http://localhost:3000 in your browser.

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System Workflows

1. Seeding & Credentials

On startup, the system seeds default values.

• Initial Super Admin: admin@netraid.ai / Admin@NetraID2026
• Default Shift Timing: Start: 09:00, End: 17:00
• Grace Period: 15 minutes.
• Similarity Thresholds: Face Cosine distance: 0.60, Liveness threshold: 0.75

2. Biometric Face Enrollment (10 Poses)

To complete a registration, navigate to the Employees page, select a user, and click Camera. The admin must capture/upload 10 distinct poses to achieve high-accuracy matches:

• Poses: Front, Left, Right, Looking Up, Looking Down, Smiling, Neutral, Indoor Light, Outdoor Light, Glasses (Optional).
• Reference embeddings are saved as L2-normalized 512-D float vectors inside PostgreSQL.

3. Kiosk Scanning & Liveness

When an employee stands in front of the kiosk webcam:

1. The system captures frames and scans for exactly one face (SCRFD).
2. Runs Silent-Face-Anti-Spoofing (MiniFASNet). If the liveness score is below 0.75, the scan is rejected as a spoof (e.g. photos, phone screen).
3. Extracts the 512-D face embedding (ArcFace) and runs a pgvector cosine distance query:

   SELECT * FROM face_embeddings 
   ORDER BY embedding <=> :search_vector LIMIT 1;
   

4. Calculates similarity 1.0 - distance. If similarity is $\ge 0.60$, the employee is identified.
5. Marks Check-In (first swipe) or Check-Out (subsequent swipes) and logs the entry.
6. Synthesizes a greeting offline using pyttsx3 (Windows SAPI5 / Linux espeak) based on time-of-day:
   • 05:00 - 11:59: "Good Morning"
   • 12:00 - 16:59: "Good Afternoon"
   • 17:00 - 23:59: "Good Evening"