Significant Parameters of the Keystroke for the Formation of the Input Field of a Convolutional Neural Network

Dychka, Ivan; Tereikovskyi, Ihor; Tereikovska, Liudmyla; Корченко, Олександр Григорович; Погорелов, В. В.

doi:10.1007/978-3-030-55506-1_45

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…В основу розробки означених процедур покладено результати робіт [2,4,6,7], в яких розроблено теоретичні засади та практичні рекомендації по застосуванню ЗНМ для аналізу динамки параметрів КП.…”

Section: вступunclassified

Way of application of convolutional neural networks for personality recognition and user emotions by keyboard handwriting

Кулаков¹,

Терейковська²,

Терейковський³

2021

Prav. norm. metrol. zabezp. sist. zahistu inf. v Ukr.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: вступunclassified