Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

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dc.contributor.authorKalyta, Oleg
dc.contributor.authorBarmak, Olexander
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorKrak, Iurii
dc.date.accessioned2023-09-12T07:24:36Z
dc.date.available2023-09-12T07:24:36Z
dc.date.issued2023-08-31
dc.description.abstractModern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video sur-veillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person’s emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric in-terpretation of facial areas to extract features of facial expression, the method of hyperplane clas-sification for identifying emotional states in the feature vector space, and the principles of visual analytics and “human in the loop” to obtain transparent and interpretable classifiers. The experi-mental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91–2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23–2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.uk_UA
dc.description.sponsorshipThis research was funded by the Ministry of Education and Science of Ukraine, state grant regis-tration number 0121U112025, project title “Development of information technology for making human-controlled critical and safety decisions based on mental-formal models of machine learning.” This publication reflects the views of the authors only, and the Ministry of Education and Science of Ukraine cannot be held responsible for any use of the information contained therein.uk_UA
dc.identifier.citationKalyta O., Barmak O., Radiuk P., Krak I. Facial emotion recognition for photo and video surveillance based on machine learning and visual analytics. Applied Sciences. 2023. Vol. 13. Issue. 17. P. 9890. (Scopus, Q2). DOI: https://doi.org/10.3390/app13179890uk_UA
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/14395
dc.language.isoenuk_UA
dc.publisherMDPIuk_UA
dc.subjectemotion recognitionuk_UA
dc.subjectfacial feature extractionuk_UA
dc.subjectvideo surveillanceuk_UA
dc.subjectmachine learninguk_UA
dc.subjectvisual analyticsuk_UA
dc.subjecthyperplane classificationuk_UA
dc.titleFacial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analyticsuk_UA
dc.typeСтаттяuk_UA
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