User Keystroke Authentication and Recognition of Emotions Based on Convolutional Neural Network

  • Ihor TereikovskyiEmail author
  • Liudmyla Tereikovska
  • Oleksandr Korystin
  • Shynar Mussiraliyeva
  • Aizhan Sambetbayeva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)


The article is devoted to the problem of improving Biometric identification systems based on Keystroke Dynamics for recognizing emotions and authenticating users of information systems through the implementation of modern neural network solutions based on Convolutional Neural Network (CNN). It is established that the difficulties of such implementation are associated with coding the keystroke parameters to a form suitable for CNN processing. A coding procedure based on the presentation of fixed-size keystroke parameters in the form of a color square image is proposed. Each encoded text symbol corresponds to a separate point of the image and is characterized using the corresponding ASCII code and keystroke parameters such as the key hold time and the time between keystrokes. Experimental studies showed that the proposed coding procedure made it possible to use CNN for analyzing Keystroke Dynamics and achieve recognition error of emotions and personality at the level of the best modern recognition systems.


Recognition of emotions Biometric authentication Keystroke Dynamics Convolutional neural network 


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KievUkraine
  2. 2.Kyiv National University of Construction and ArchitectureKievUkraine
  3. 3.Scientifically Research Institute of the Ministry of Internal AffairsKievUkraine
  4. 4.Al-Farabi Kazakh National UniversityAlmatyKazakhstan

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