🚀 Hackathon Finalist Project
An intelligent, secure, and scalable authentication system that combines biometrics, device fingerprinting, and AI-based anomaly detection to enhance modern authentication beyond traditional passwords and OTPs.
Traditional authentication methods are vulnerable to attacks like phishing, brute force, and credential stuffing.
This project introduces a multi-layered authentication system that uses AI/ML to improve both security and user experience.
- PIN-based authentication
- Biometric face verification
- Device fingerprinting
- Deep learning-based identity verification
- Detects spoofing (photos/videos)
- Identifies unique devices using system metadata
- Detects suspicious login attempts
- Tracks login patterns (IP, device, behavior)
- Blocks suspicious activity after multiple failed attempts
- Sends OTP/code via Flask-Mail
Backend: Python (Flask)
AI/ML: DeepFace, TensorFlow/Keras, MTCNN, dlib, face_recognition
Computer Vision: OpenCV, MediaPipe
Database: PostgreSQL (psycopg2)
Other: Docker, Device Fingerprinting (custom module)
multi-factor-authentication-system/
├── src/ # Core logic
├── database/ # DB operations
├── models/ # ML models
├── templates/ # HTML files
├── static/ # CSS & JS
├── main.py # Flask app
├── config.py
├── requirements.txt
git clone https://github.com/sarasakeena/multi-factor-authentication-system.git
cd multi-factor-authentication-system- Create Virtual Environment
python -m venv venv
venv\Scripts\activate # Windows
- Install Dependencies
pip install -r requirements.txt
- Create .env File
FLASK_SECRET_KEY=your_secret_key
DB_NAME=your_db_name
DB_USER=your_db_user
DB_PASSWORD=your_password
DB_HOST=localhost
DB_PORT=5432
MAIL_USERNAME=your_email@gmail.com
MAIL_PASSWORD=your_app_password
- Run the App
python main.py
- Open in Browser
http://127.0.0.1:5000/
User registers with face + PIN
Device fingerprint is stored
Login includes:
Face verification + liveness detection
Device validation
Risk analysis
Suspicious attempts → blocked
Cloud deployment (AWS / GCP)
JWT-based authentication
Advanced ML-based anomaly detection
Mobile app integration
Sensitive data (.env, user images) are excluded
Developed as a hackathon finalist project
Sara Sakeena GitHub: https://github.com/sarasakeena
If you like this project, give it a ⭐!