TWO-FACTOR USER AUTHENTICATION SYSTEM USING FACIAL RECOGNITION

Abstract

In today's world of information technology, data security is becoming a paramount concern. One of the most effective protection methodologies is two-factor authentication. This article delves into a cutting-edge method of two-factor authentication based on the combination of neural networks and facial recognition. Deep learning, employed in neural networks, allows the system to adapt to minor changes in a user's appearance, such as a new hairstyle, the presence or absence of makeup, wearing glasses, and so on. This makes the system flexible and capable of recognizing the user even with slight alterations in their look. The core idea of the method is to analyze the unique features of the user's face. The neural network "learns" the characteristics of each user, creating their unique "portrait". This "portrait" is then used for identity verification upon attempting to access the system. In addition to facial recognition, the system may require password input or another form of authentication, making the login process even more secure. The combination of these two methods ensures a high level of protection against unauthorized access. A significant advantage of such a system is its convenience for the user. The user's face becomes the "key" to the system, making the login process quick and seamless. It's also worth noting that the advancement of facial recognition technology opens new horizons for data security. Using neural networks in conjunction with two-factor authentication may become the standard in the near future.