In today's world, digital technologies are penetrating all aspects of human activity, including education and labor. Since 2019, when, in response to global challenges, the world's educational systems have actively started to shift to distance learning, there has been an urgent need to develop and implement reliable identification and authentication technologies. These technologies are necessary to ensure the authenticity of work and protection from falsification of academic achievements, especially in the context of higher education in accordance with the group of specialties and directions (USGS) 10.00.00 - Information Security, where laboratory and practical work play a key role in the educational process.

The problem lies in the need to optimize the flow of incoming data, which, first, can affect the retraining of the neural network core of the recognition system, and second, impose excessive requirements on the network's bandwidth. To solve this problem, efficient preprocessing of gesture data is required to simplify their trajectories while preserving the key features of the gestures.

This article proposes the use of the Douglas–Peucker algorithm for preliminary processing of mouse gesture trajectory data. This algorithm significantly reduces the number of points in the trajectories, simplifying them while preserving the main shape of the gestures. The data with simplified trajectories are then used to train neural networks.

The experimental part of the work showed that the application of the Douglas–Peucker algorithm allows for a 60% reduction in the number of points in the trajectories, leading to an increase in gesture recognition accuracy from 70% to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency.

The study confirmed the effectiveness of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition tasks. The article suggests directions for further research, including the optimization of the algorithm's parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods. The obtained results can be applied to developing more intuitive and adaptive user interfaces.