Research on Methods for Development of Software System for Emotions Recognition and State of Human Health Determination

Abstract

To determine the general state of human health using emotion recognition, a method based on machine learningwas chosen, the classifier is trained on the dataset “fer2013” and “PAB-F”. negative emotions and the use of a dataset totrain the neural network, with images of people divided into classes “painful” - “not painful”. For the “fer2013” dataset,it is necessary to determine the presence of pain after data processing. As a rule, pain is expressed in the intense andprolonged presence of emotions of anger and sadness. I suggest measuring the intensity of emotion by the probabilityfactor of determining the emotion, which returns the neural network. It has been experimentally determined that aspecialized data set better copes with the task, despite the fact that it has a sufficiently small number of images. “Fer2013”has a high percentage of false positives. This can be explained by the fact that the photos also showed obvious negativeemotions, which were regarded as pain. The number of learning epochs has a positive effect on the accuracy of theneural network, and the increase in learning speed has a negative effect. To check the accuracy of the neural network atthe entrance to the module to check the emotional state at the entrance were images found by the search query “Paingrimace” on the resource depositphotos.