Robust Fingerprint Verification-Using Real Time Security Alerts

This work presents Robust Fingerprint Authentication with Real-Time Security Alerts, a biometric authentication system that integrates Aadhaar-based identity verification, liveness detection, and real-time SMS notifications. Traditional fingerprint authentication systems store biometric data persistently, increasing the risk of breaches and unauthorized access. To address this, the proposed system ensures immediate deletion of fingerprint data after verification, enhancing security and user privacy. Advanced image processing techniques and deep learning models are employed for liveness detection, mitigating spoofing attacks using fake fingerprints. An OTP-based authentication mechanism adds an additional security layer, while real-time SMS notifications inform users of authentication attempts, improving transparency. The system is implemented using Flask, PostgreSQL, OpenCV, Twilio, and TensorFlow, ensuring scalability, efficiency, and secure data handling. HTTPS encryption and compliance with GDPR and CCPA regulations further strengthen security by enforcing responsible biometric data processing. Experimental results demonstrate 98.5% fingerprint matching accuracy, 97.8% liveness detection success, and 99.3% OTP verification accuracy, surpassing traditional fingerprint authentication models. By integrating multi-layered security features and real-time user alerts, this system provides a privacy-conscious, secure, and efficient biometric authentication solution suitable for applications in banking, government services, and enterprise security