Recognition of emotional expressions using the grouping crowdings of characteristic mimic states

• Datum: 2020
• Hauptverfasser: Barmak, O.V., Manziuk, E.A., Kalyta, O.D., Krak, Iu.V., Kuznetsov, V.O., Kulias, A.I.
• Format: Artikel
• Sprache: Ukrainian
• Veröffentlicht: PROBLEMS IN PROGRAMMING 2020
• Schlagworte: facial expressions emotion recognition; hyperplane classification; a simplified classification model; UDC 004.912
• Online Zugang: https://pp.isofts.kiev.ua/index.php/ojs1/article/view/409
• Назва журналу: Problems in programming

The characteristic forms of facial expressions of the emotional states of a person are typical of a rather large degree of generalization on the basis of common physiological structures and the location of the muscles that form the human face. This circumstance is one of the main reasons for the commonality of human manifestations of emotions that are reflected in the face. By the nature and form of facial expressions on the face with high probability, it is possible to determine the emotional state of a person with some correction on the part of the cultural characteristics and traditions of certain groups. In accordance with the existence of common mimic forms of emotional manifestations, an approach is proposed to create a model of recognition of emotional manifestations on the face of a person with relatively low requirements for the means of photo, video-fixation and acceptable speed in the video stream. The creation of the model is based on the implementation of the hyperplane classification of mimic manifestations of major emotional states. One of the main advantages of the proposed approach is the small computational complexity that allows realizing the recognition of the changes in people’s emotional state without any special equipment (for low-resolution or long-distance video cameras).  In addition, the model developed on the basis of the proposed approach allows obtaining proper recognition accuracy with low requirements for quality image characteristics, which allows extending the scope of practical application to a great extent. One example of practical application is control over the drivers in the process of driving the vehicle, complex production operators, and other automated visual surveillance systems. The set of detected emotional states is formed in accordance with the set tasks and gives the opportunity to focus on the recognition of mimic forms and group characteristic structural manifestations based on the set of distinguished characteristic features.Problems in programming 2020; 2-3: 173-181