{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T22:51:35Z","timestamp":1766875895824,"version":"3.37.3"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["18KJA520012","KC19197","XCX2020002"],"award-info":[{"award-number":["18KJA520012","KC19197","XCX2020002"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018636","name":"Xuzhou Science and Technology Plan Project","doi-asserted-by":"crossref","award":["18KJA520012","KC19197","XCX2020002"],"award-info":[{"award-number":["18KJA520012","KC19197","XCX2020002"]}],"id":[{"id":"10.13039\/501100018636","id-type":"DOI","asserted-by":"crossref"}]},{"name":"One Stop Service Platform of Pocket Campus Based on Wechat Applet","award":["18KJA520012","KC19197","XCX2020002"],"award-info":[{"award-number":["18KJA520012","KC19197","XCX2020002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,3,2]]},"abstract":"<jats:p>At present, traditional facial expression recognition methods of convolutional neural networks are based on local ideas for feature expression, which results in the model\u2019s low efficiency in capturing the dependence between long-range pixels, leading to poor performance for facial expression recognition. In order to solve the above problems, this paper combines a self-attention mechanism with a residual network and proposes a new facial expression recognition model based on the global operation idea. This paper first introduces the self-attention mechanism on the basis of the residual network and finds the relative importance of a location by calculating the weighted average of all location pixels, then introduces channel attention to learn different features on the channel domain, and generates channel attention to focus on the interactive features in different channels so that the robustness can be improved; finally, it merges the self-attention mechanism and the channel attention mechanism to increase the model\u2019s ability to extract globally important features. The accuracy of this paper on the CK+ and FER2013 datasets is 97.89% and 74.15%, respectively, which fully confirmed the effectiveness and superiority of the model in extracting global features.<\/jats:p>","DOI":"10.1155\/2021\/6624251","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T20:50:13Z","timestamp":1614804613000},"page":"1-10","source":"Crossref","is-referenced-by-count":14,"title":["Facial Expression Recognition Based on Attention Mechanism"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0286-5417","authenticated-orcid":true,"given":"Jiang","family":"Daihong","sequence":"first","affiliation":[{"name":"Xuzhou University of Technology, College of Information Engineering, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Hu yuanzheng","sequence":"additional","affiliation":[{"name":"Xuzhou University of Technology, College of Information Engineering, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dai","family":"Lei","sequence":"additional","affiliation":[{"name":"Xuzhou University of Technology, College of Information Engineering, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[{"name":"Xuzhou University of Technology, College of Information Engineering, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"Deep facial expression recognition: a survey","author":"S. Li","year":"2020","journal-title":"IEEE Transactions on Affective Computing"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1134\/s1054661807040190"},{"key":"3","first-page":"38","article-title":"Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition","volume":"41","author":"R. Zhi","year":"2010","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics Part B"},{"first-page":"2562","article-title":"Learning active facial patches for expression analysis","author":"L. Zhong","key":"4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/s0893-6080(03)00115-1"},{"first-page":"472","article-title":"Holonet: towards robust emotion recognition in the wild","author":"A. Yao","key":"6"},{"key":"7","first-page":"317","article-title":"Feature selection mechanism in cnns for facial expression recognition","author":"S. Zhao","year":"2018","journal-title":"BMVC"},{"first-page":"3359","article-title":"Joint pose and expression modeling for facial expression recognition","author":"F. Zhang","key":"8"},{"first-page":"302","article-title":"Island loss for learning discriminative features in facial expression recognition","author":"J. Cai","key":"9"},{"key":"10","first-page":"1578","article-title":"Research on expression recognition based on improved deep residual network","volume":"37","author":"L. He","year":"2020","journal-title":"Journal of Applied Computing Research"},{"first-page":"2983","article-title":"Joint fine-tuning in deep neural networks for facial expression recognition","author":"H. Jung","key":"11"},{"first-page":"1805","article-title":"Facial expression recognition via a boosted deep belief network","author":"P. Liu","key":"12"},{"first-page":"3391","article-title":"Deep region and multi-label learning for facial action unit detection","author":"K. Zhao","key":"13"},{"key":"14","first-page":"370","article-title":"Convolution neural network with multi-resolution feature fusion for facial expression recognition","volume":"55","author":"Z. He","year":"2018","journal-title":"Laser Optoelectronics Program"},{"key":"15","first-page":"338","article-title":"Facial expression recognition based on convolutional neural network local feature fusion","volume":"57","author":"X. Yao","year":"2020","journal-title":"Program Laser Optoelectronics"},{"key":"16","first-page":"2204","article-title":"Recurrent models of visual attention","volume-title":"Advances in Neural Information Processing Systems","author":"V. Mnih","year":"2014"},{"first-page":"7354","article-title":"Self-attention generative adversarial networks","author":"H. Zhang","key":"17"},{"key":"18","first-page":"2017","article-title":"Spatial transformer networks","volume-title":"Advances in Neural Information Processing Systems","author":"M. Jaderberg","year":"2015"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.034"},{"key":"20","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1109\/TIP.2019.2948288","article-title":"Attended end-to-end architecture for age estimation from facial expression videos","volume":"29","author":"W. Pei","year":"2019","journal-title":"IEEE Transactions on Image Process"},{"first-page":"770","article-title":"Deep residual learning for image recognition","author":"K. He","key":"21"},{"first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"M. D. Zeiler","key":"22"},{"article-title":"Tensorflow: large-scale machine learning on heterogeneous distributed systems","year":"2016","author":"M. Abadi","key":"23"},{"article-title":"Challenges in representation learning: a report on three machine learning contests","author":"I. J. Goodfellow","key":"24","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-42051-1_16"},{"author":"P. Lucey","key":"25","article-title":"The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression"},{"article-title":"Deep learning using linear support vector machines","year":"2013","author":"Y. Tang","key":"26"},{"first-page":"279","article-title":"A deep-learning approach to facial expression recognition with candid images","author":"W. Li","key":"27"},{"article-title":"Facial expression recognition based on complexity perception classification algorithm","year":"2018","author":"T. Chang","key":"28"},{"first-page":"1","article-title":"Going deeper in facial expression recognition using deep neural networks","author":"A. Mollahosseini","key":"29"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1049\/el.2016.4328"},{"first-page":"143","article-title":"Deeply learning deformable facial action parts model for dynamic expression analysis","author":"M. Liu","key":"31"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/6624251.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/6624251.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/6624251.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T20:50:38Z","timestamp":1614804638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2021\/6624251\/"}},"subtitle":[],"editor":[{"given":"Qinhu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":31,"alternative-id":["6624251","6624251"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6624251","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"type":"electronic","value":"1875-919X"},{"type":"print","value":"1058-9244"}],"subject":[],"published":{"date-parts":[[2021,3,2]]}}}