{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:49:33Z","timestamp":1701478173984},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>In view of the lack of facial expression data set in the classroom environment, the classroom expression data set was constructed, including the acquisition and preprocessing of students\u2019 face pictures, the selection of students\u2019 emotional categories in the classroom environment and the labeling of pictures. Based on the Resnet50 network model, a network structure with attention module is proposed, so that it can focus on the feature parts that clearly represent the target emotion in facial images, so as to enhance the accuracy of facial emotion recognition. In order to verify the effect of the model presented in this paper, training tests were carried out on the common data set of expression Fer2013 and the classroom data set constructed in this article. The results show that the structural model presented in this paper has better recognition effect and can effectively enhance the accuracy of expression recognition.<\/jats:p>","DOI":"10.3233\/faia230873","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:45Z","timestamp":1701446145000},"source":"Crossref","is-referenced-by-count":0,"title":["Facial Expression Recognition in Classroom Environment Based on Attention Mechanism"],"prefix":"10.3233","author":[{"given":"Yue","family":"Wu","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Jilin Engineering Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230873","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:46Z","timestamp":1701446146000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230873","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}