Open AccessWay of application of convolutional neural networks for personality recognition and user emotions by keyboard handwriting
Author(s) -
Юрій Олексійович Кулаков,
Людмила Олексіївна Терейковська,
Ігор Анатолійович Терейковський
Publication year - 2021
Publication title -
pravove, normativne ta metrologìčne zabezpečennâ sistemi zahistu ìnformacìï v ukraïnì
Language(s) - English
Resource type - Journals
eISSN - 2411-0671
pISSN - 2074-9481
DOI - 10.20535/2074-9481.2(38).2019.232654
Subject(s) - computer science , handwriting , convolutional neural network , artificial neural network , field (mathematics) , facial recognition system , artificial intelligence , face (sociological concept) , representation (politics) , pattern recognition (psychology) , intelligent character recognition , speech recognition , machine learning , image (mathematics) , social science , mathematics , sociology , politics , political science , pure mathematics , law , character recognition
An important direction of increasing the security and expanding the functionality of modern information systems is the introduction of face recognition tools and user emotions by their keyboard handwriting. The expediency of improving the indicated recognition means by introducing modern neural network solutions into them is shown. A way has been developed for using a convolutional neural network for recognizing a user's face and emotions from keyboard handwriting, the features of which are the procedure for adapting the structural parameters of a convolutional neural network of the VGG type to the expected conditions of use and a procedure for determining the input field, which provides the representation of the parameters of colored channels. After adapting the structural parameters, the VGG network was implemented using the MATLAB R2018b application package, which made it possible to carry out computer experiments aimed at verifying the proposed method. As a result of the conducted computer experiments, it was determined that the use of the proposed method of applying a convolutional neural network makes it possible to achieve a user face recognition accuracy of about 82% with 50 learning epochs. The need for further research in the direction of the formation of a training sample is shown, which will ensure high-quality training of the neural network model.