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Person Authentication using Visual Representations of Keyboard Typing Dynamics

Publication at Faculty of Mathematics and Physics |
2022

Abstract

In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch.

Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines.

Moreover, the proposed visual representation of the data contributes to the explainability of AI.