Securing ATM Transactions with Facial Recognition-Based Verification System

Abstract: The current ATM authentication method through PINs exposes users to vulnerabilities such as stolen PINs and cloned cards in traditional systems. This project introduces a Face ATM System that improves safety through deep learning facial identification and mobile authentication instead of the current PIN-based systems. Users receive Face Verification Links through their mobile phones to establish secure account access after CNNs verify their faces. The system delivers security alerts in real-time while keeping banks notified about each transaction to detect problematic behaviour. The system, developed with Python, Flask, OpenCV, and MySQL, presents a security-focused and fraud-resistant method that enhances the security profile and user experience of ATM transactions.

Keywords: Deep Learning, CNN, Biometric Authentication, Mobile Verification, AI-driven Authentication.