Keyboard Dynamic Analysis by Alexnet Type Neural Network

Abstract

In this article has been reviewed questions of development neural network analysis tools of keyboard handwriting indicators for personality and user emotions recognition. Installed ability to upgrade specified funds through the use of convolutional neural networks of AlexNet type, which makes it necessary to evaluate the effectiveness of such use. It was also determined that it is possible to evaluate the efficiency of using the neural network model experimentally with using indicators of recognition accuracy and duration of training. A software implementation of AlexNet was developed, and a training sample was formed, consisting of 1005 examples of the parameters of the dynamics of keyboard handwriting for 10 users. As parameters characterizing of the dynamics of keyboard handwriting has been used holding time and the time between successive pressing of two keys. Using computer experiments, it was found that in a fairly limited training sample at 50 training epochs, AlexNet allows achieving user recognition accuracy of over 80%, which is comparable to the results of the best modern systems of similar purpose and confirms the possibility of effective use of this type of network for analyzing the dynamics of keyboard handwriting. The need for further research in the direction of the formation of the training sample, that providing high-quality training of the neural network model is shown. The expediency of developing a method for adapting AlexNet architectural parameters to the conditions of the task of analyzing the dynamics of keyboard handwriting was also determined.