Beyond passwords: A multi-factor authentication approach for robust digital security

Multi-Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email-password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.