Biometric Two-Factor Authentication Method Using Liveliness Detection with Human Presence Indicators

In the ever-evolving landscape of information security, there's a continuous effort to create innovative identification methods and tools, biometrics included. However, given the rising number of assaults on these tools, it's crucial to combine optical image recognition, leveraging neural network technologies, with the ascertainment of a live individual's traits through thermal imaging. This necessitates the utilization of existing biometric data arrays. There are multiple such systems, with the most advanced being neural network-based authentication that analyzes thermal graphical scanning data of personal biometric information, a method already employed in authentication and identification systems. To ensure the protection of information in information systems, a comprehensive security framework is implemented, in which methods to prevent unauthorized access (UAA) play a crucial role. The cornerstone of software and hardware tools for UAA protection lies in identification (verifying identity) and authentication (confirming authenticity) procedures for users. The most prevalent user authentication method involves using specific information (password), which is user-friendly but also highly susceptible to breaches. To bolster the efficacy of user identification security, biometric authentication methods, including those that ascertain the attributes of a live individual, have been incorporated recently [4–8]. The suggested approach pertains to automation and computer technology and can be applied in systems for automated identification and verification of individuals based on facial images. The result is achieved by combining the optical image recognition method, which leverages a deep learning algorithm of an artificial computational neural network, with the identification of the characteristics of a living person using thermal imaging.