Abstract
This paper deals with the task of human emotion recognition in images. The authors analyzed and reviewed the results of other researchers in this task and provided a brief overview of the machine learning and deep learning methods used in the study. Dataset of images of people in seven different emotional states was selected for the experiments. The authors proposed various features for machine learning and deep learning models using information about the location of anthropometric points on a human face. Based on each of the proposed features, the authors created datasets on which they tested various machine learning methods and different architectures of perceptrons. The results of numerical experiments presented in the paper are classification metrics and confusion matrices received with considered models. The model that provides the highest f1 score was selected as the best model.
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