Methods of the development of the architecture of the neural networks for identification and authentication of individuals

Abstract

This paper deals with the neural network methods of the implementation of systems of identification of individuals based on videos and photographs. Over the last few decades, it has been considered to be one of the most powerful tools and has become very popular in the literature as it is able to handle a huge amount of data. The neural network architectures used in modern biometric identification systems have been reviewed. Based on the research conducted in this field, an approach was developed that can improve the accuracy of object recognition in photo and video images by increasing the quality of the attributes of the weights and reducing the number of the weights, as well as the number of the connections. The basis of the developed neural network model is a multilayer perceptron; the main system is a convolutional neural network. The neural network model has been implemented using the Python programming language with the most popular machine learning libraries Keras and TensorFlow. In addition, we will also enumerate the parameters that affect CNN efficiency.