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Mask and Emotion: Computer Vision in the Age of COVID-19

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dc.contributor.author Семеріков, Сергій Олексійович
dc.contributor.author Vakaliuk, Tetiana A.
dc.contributor.author Мінтій, Ірина Сергіївна
dc.contributor.author Гаманюк, Віта Анатоліївна
dc.contributor.author Соловйов, Володимир Миколайович
dc.contributor.author Бондаренко, Ольга Володимирівна
dc.contributor.author Нечипуренко, Павло Павлович
dc.contributor.author Шокалюк, Світлана Вікторівна
dc.contributor.author Моісеєнко, Наталя Володимирівна
dc.contributor.author Ruban, Vitalii R.
dc.contributor.author Вакалюк, Тетяна Анатоліївна
dc.contributor.author Рубан, Віталій Романович
dc.date.accessioned 2023-01-02T11:28:09Z
dc.date.available 2023-01-02T11:28:09Z
dc.date.issued 2022-04-11
dc.identifier.citation Semerikov S. O. Mask and Emotion: Computer Vision in the Age of COVID-19 / Serhiy O. Semerikov, Tetiana A. Vakaliuk, Iryna S. Mintii, Vita A. Hamaniuk, Vladimir N. Soloviev, Olga V. Bondarenko, Pavlo P. Nechypurenko, Svitlana V. Shokaliuk, Natalia V. Moiseienko, Vitalii R. Ruban // DHW 2021: Digital Humanities Workshop, Kyiv, Ukraine, 23 December 2021. – New York, NY, United States : Association for Computing Machinery, 2021. – P. 103-124. – DOI : 10.1145/3526242.3526263. – (ACM International Conference Proceeding Series) uk
dc.identifier.isbn 978-1-4503-8736-1
dc.identifier.uri https://doi.org/10.1145/3526242.3526263
dc.identifier.uri http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/6993
dc.description 2021. Google Ngram Viewer. https://books.google.com/ngrams/graph?content=computer+vision%2C+machine+vision&year_start=1800&year_end=2019&corpus=26&smoothing=3&direct_url=t1%3B%2Ccomputer%20vision%3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0#t1%3B%2Ccomputer%20vision%3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0 Adaptive Vision. 2021. Libraries comparison. https://docs.adaptive-vision.com/avl/technical_issues/LibrariesComparison.html Lakshya Agarwal, Manan Mukim, Harish Sharma, Amit Bhandari, and Atul Mishra. 2021. Face Recognition Based Smart and Robust Attendance Monitoring using Deep CNN. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). 699–704. https://doi.org/10.1109/INDIACom51348.2021.00124 Dana H. Ballard and Christopher M. Brown. 1982. Computer Vision. Prentice Hall, Englewood Cliffs. https://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm Jim Bennett. 2020. Happy, Sad, Angry Workshop. https://github.com/jimbobbennett/HappySadAngryWorkshop J. J. Gibson. 1950. The Perception of the Visual World. Boston. Google Cloud. 2021. Vision API Product Search pricing. https://cloud.google.com/vision/product-search/pricing Gunnar Rutger Grape. 1973. Model Based (Intermediate-Level) Computer Vision. Technical Report AD 763673. Stanford University. https://apps.dtic.mil/sti/citations/AD0763673 Nico Klingler. 2021. Top 8 Applications of Computer Vision in the Education Sector. https://viso.ai/applications/computer-vision-in-education/ Simon J. D. Prince. 2012. Computer Vision: Models, Learning, and Inference. Cambridge University Press. Juliet R. C. Pulliam, Cari van Schalkwyk, Nevashan Govender, Anne von Gottberg, Cheryl Cohen, Michelle J. Groome, Jonathan Dushoff, Koleka Mlisana, and Harry Moultrie. 2021. Increased risk of SARS-CoV-2 reinfection associated with emergence of the Omicron variant in South Africa. medRxiv (2021). https://doi.org/10.1101/2021.11.11.21266068 Ashwin Raj, Aparna Raj, and Imteyaz Ahmad. 2021. Smart Attendance Monitoring System with Computer Vision Using IOT. Journal of Mobile Multimedia 17, 1-3 (2021), 115–125. https://doi.org/10.13052/jmm1550-4646.17135 Mahdi Rezaei and Mohsen Azarmi. 2020. DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Applied Sciences 10, 21, Article 7514(2020). https://doi.org/10.3390/app10217514 Lawrence Gilman Roberts. 1963. Machine perception of three-dimensional solids. Thesis (Ph. D.). Massachusetts Institute of Technology. https://dspace.mit.edu/bitstream/handle/1721.1/11589/47 Connor Shorten, Taghi M. Khoshgoftaar, and Borko Furht. 2021. Deep Learning applications for COVID-19. Journal of Big Data 8, 1 (11 Jan 2021), 18. https://doi.org/10.1186/s40537-020-00392-9 S A Sivakumar, Tegil J John, G Thamarai Selvi, Bhukya Madhu, C Udhaya Shankar, and K P Arjun. 2021. IoT based Intelligent Attendance Monitoring with Face Recognition Scheme. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). 349–353. https://doi.org/10.1109/ICCMC51019.2021.9418264 Ivan Edward Sutherland. 1963. Sketchpad, a man-machine graphical communication system. Ph. D. Dissertation. Massachusetts Institute of Technology. http://images.designworldonline.com.s3.amazonaws.com/CADhistory/Sketchpad_A_Man-Machine_Graphical_Communication_System_Jan63.pdf Viktoriia Tkachuk, Yuliia Yechkalo, Serhiy Semerikov, Maria Kislova, and Yana Hladyr. 2021. Using Mobile ICT for Online Learning During COVID-19 Lockdown. In Information and Communication Technologies in Education, Research, and Industrial Applications, Andreas Bollin, Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, Mykola Tkachuk, Vitaliy Yakovyna, and Grygoriy Zholtkevych (Eds.). Springer International Publishing, Cham, 46–67. https://doi.org/10.1007/978-3-030-77592-6_3 viso.ai. 2021. Abandoned Luggage. https://viso.ai/application/abandoned-luggage-detection/ viso.ai. 2021. Face Recognition. https://viso.ai/application/face-recognition/ viso.ai. 2021. Facial Emotion Analysis. https://viso.ai/application/emotion-analysis/ viso.ai. 2021. Intrusion Detection. https://viso.ai/application/intrusion-detection/ viso.ai. 2021. Mask Detection: Automatically detect unmasked people in public spaces or indoors. https://viso.ai/application/mask-detection/ viso.ai. 2021. Parking Lot Occupancy. https://viso.ai/application/parking-lot-occupancy-detection/ viso.ai. 2021. Social Distancing Monitoring. https://viso.ai/application/social-distancing-monitoring/ uk
dc.description.abstract Computer vision systems since the early 1960s have undergone a long evolution and are widely used in various fields, in particular, in education for the implementation of immersive educational resources. When developing computer vision systems for educational purposes, it is advisable to use the computer vision libraries based on deep learning (in particular, implementations of convolutional neural networks). Computer vision systems can be used in education both under normal and pandemic conditions. The changes in the education industry caused by the COVID-19 pandemic have affected the classic educational applications of computer vision systems, modifying existing ones and giving rise to new ones, including social distancing, face mask recognition, intrusion detection in universities and schools, and vandalism prevention, recognition of emotions on faces with and without masks, attendance monitoring. Developed on the basis of Microsoft Cognitive Toolkit and deployed in the Microsoft Azure cloud, a prototype computer vision system integrates emotion recognition of students and detection of violations of the mask regime, additionally providing the ability to determine gender, smile intensity, average age, makeup, glasses, hair color, etc. with a high degree of reliability. uk
dc.language.iso en uk
dc.publisher Association for Computing Machinery uk
dc.subject computer vision uk
dc.subject COVID-19 uk
dc.subject education uk
dc.subject mask detection uk
dc.title Mask and Emotion: Computer Vision in the Age of COVID-19 uk
dc.type Article uk


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