METHOD OF BIOMETRIC AUTHENTICATION OF PERSONNEL OF CRITICAL INFRASTRUCTURE FACILITIES BY FACIAL IMAGE AND IRIS OF THE EYE USING NEURAL NETWORK TOOLS

The widespread implementation of neural network biometric authentication tools based on facial and iris images at critical infrastructure facilities has made it possible to increase the level of security and efficiency of personnel identification. At the same time, modern requirements dictate the need to increase resistance to spoofing attacks, adaptability to interference from the real environment, as well as expand functionality to take into account the psycho-emotional state of personnel at the time of authentication. Traditional neural network methods based on neural networks with monolithic architecture are limited in implementing these requirements due to insufficient flexibility and difficulty in adapting to variable video recording conditions. Therefore, this article proposes a method for biometric authentication of personnel at critical infrastructure facilities based on facial and iris images using neural network tools. The method implements multi-step adaptive processing of the video stream and comprehensive execution of procedures necessary for effective authentication in conditions of variability of image parameters. The stages of the method are structured as a sequenc