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

Authors

  • O. Y. Golikov Laboratory Low Temperatures, Department of Condensed Matter Physics, Condensed Matter Physics Center (IFIMAC) and Nicolás Cabrera Institute, Autonomous University of Madrid, 7, Francisco Tomás y Valiente, 28049 Madrid, Spain
  • M. A. Ramos Laboratory Low Temperatures, Department of Condensed Matter Physics, Condensed Matter Physics Center (IFIMAC) and Nicolás Cabrera Institute, Autonomous University of Madrid, 7, Francisco Tomás y Valiente, 28049 Madrid, Spain

DOI:

https://doi.org/10.26577/phst.2020.v7.i2.07
        37 69

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.

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How to Cite

Golikov, O. Y., & Ramos, M. A. (2020). Methods of the development of the architecture of the neural networks for identification and authentication of individuals. Physical Sciences and Technology, 7(3-4), 44–49. https://doi.org/10.26577/phst.2020.v7.i2.07

Issue

Vol. 7 No. 3-4 (2020): PHYSICAL SCIENCES AND TECHNOLOGY

Section

Nuclear Physics and Related Techology

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