AI-Multi-Factor Authentication System with Live QRCode Scanning and Facial Liveness Detection

Conventional single-factor authentication techniques, like ID cards or passwords, are becoming more and more susceptible to spoofing, identity theft, and illegal access. We suggest a multi factor authentication (MFA) system that combines geolocation verification, facial recognition with liveness detection, QRCode scanning, and anti-tailgating monitoring into a single web-based access control solution in order to overcome these difficulties. In order to prevent tailgating, the system uses QRCode-based user identification, blink-based liveness verification using LBPH face recognition and Haarcascade, geolocation validation using haversine distance calculations, and Mobile Net SSD-based person detection. The suggested architecture provides a dependable, scalable, and reasonably priced method of enhancing authentication security in critical infrastructure environments, educational institutions, and commercial settings. With a 96.5% overall success rate, the proposed web-based multi-factor authentication system successfully combines facial recognition, geolocation, and QRCode technologies. The system's 96.5% facial recognition accuracy, 99.5 % fake-face detection rate, and 95.8% tailgating detection rate attest to its dependability and practical utility in real-world situations.