The application of deep learning convolutional neural networks for solving the problem of automated facial expression recognition and determination of emotions of a person is analyzed. It is proposed to use the advantages of the transfer approach to deep learning convolutional neural networks training to solve the problem of insufficient data volume in sets of images with different facial expressions. Most of these datasets are labeled in accordance with a facial coding system based on the units of human facial movement. The developed technology of transfer learning of the public deep learning convolutional neural networks families DenseNet and MobileNet, with the subsequent “fine tuning” of the network parameters, allowed to reduce the training time and computational resources when solving the problem of facial expression recognition without losing the reliability of recognition of motor units. During the development of deep learning technology for convolutional neural networks, the following tasks were solved. Firstly, the choice of publicly available convolutional neural networks of the DenseNet and MobileNet families pre-trained on the ImageNet dataset was substantiated, taking into account the peculiarities of transfer learning for the task of recognizing facial expressions and determining emotions. Secondary, a model of a deep convolutional neural network and a method for its training have been developed for solving problems of recognizing facial expressions and determining human emotions, taking into account the specifics of the selected pretrained convolutional neural networks. Thirdly, the developed deep learning technology was tested, and finally, the resource intensity and reliability of recognition of motor units on the DISFA set were assessed. The proposed technology of deep learning of convolutional neural networks can be used in the development of systems for automatic recognition of facial expressions and determination of human emotions for both stationary and mobile devices. Further modification of the systems for recognizing motor units of human facial activity in order to increase the reliability of recognition is possible using of the augmentation technique.

The relevance of solving the problem of recognizing facial expressions in the image of a person's face for the formation of amodel of social interactions in the development of intelligent systems for computer vision, human-machine interaction, online learning, emotional marketing, and game intelligence is shown. The aim of the work is to reduce the training time and computational resources without losing the reliability of the multivalued classification of motor units for solving the problem of facial expression recognition in a human face image by developing an adaptive model of a convolution neural network and a method for its training with “fine tuning” of parameters. To achieve the goal, several tasks were solved in the work. Models of specialized convolution neural networks and pre-trained on the ImageNet set were investigated. The stages of transfer learning of convolution neural networks were shown. A model of a convolutionalneural network and a method for its training were developed to solve the problems of facial expression recognition on a human face image. The reliability of recognition of motor units was analyzed based on the developed adaptive model of a convolution neural network and the method of its transfer learning. It is shown that, on average, the use of the proposed loss function in a fully connected layer of a multi-valued motor unit classifier within the framework of the developed adaptive model of a convolution neural network based on the publicly available MobileNet-v1 and its transfer learning method made it possible to increase the reliability of solving the problem of facial expression recognition inahuman face image by 6 % by F1 value estimation.

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