{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:34:05Z","timestamp":1778603645694,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Educ Inf Technol"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10639-022-11370-4","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T01:02:25Z","timestamp":1665450145000},"page":"4069-4092","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learning"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1481-094X","authenticated-orcid":false,"given":"Imane","family":"Lasri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anouar","family":"Riadsolh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mourad","family":"Elbelkacemi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"11370_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., & et al. (2016). Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265\u2013283)."},{"key":"11370_CR2","unstructured":"Aifanti, N., Papachristou, C., & Delopoulos, A. (2010). The mug facial expression database. In Proceedings of the 11th international workshop on image analysis for multimedia interactive services (WIAMIS) (pp. 1\u20134). Desenzano del Garda, Italy: IEEE."},{"key":"11370_CR3","doi-asserted-by":"publisher","unstructured":"Aslan, S., Alyuz, N., Tanriover, C., Mete, S., Okur, E., D\u2019Mello, S., & Arslan Esme, A. (2019). Investigating the impact of a real-time, multi- modal student engagement analytics technology in authentic classrooms. In Proceedings of the 2019 conference on human factors in computing systems (chi). https:\/\/doi.org\/10.1145\/3290605.3300534 (pp. 1\u201312). Glasgow Scotland, UK: ACM.","DOI":"10.1145\/3290605.3300534"},{"issue":"11","key":"11370_CR4","doi-asserted-by":"publisher","first-page":"0258788","DOI":"10.1371\/journal.pone.0258788","volume":"16","author":"S Ayouni","year":"2021","unstructured":"Ayouni, S., Hajjej, F., Maddeh, M., & Al-Otaibi, S. (2021). A new ml-based approach to enhance student engagement in online environment. PLoS ONE, 16(11), 0258788. https:\/\/doi.org\/10.1371\/journal.pone.0258788.","journal-title":"PLoS ONE"},{"key":"11370_CR5","unstructured":"Bradski, G. (2000). The opencv library. Dr. Dobb\u2019s Journal of Software Tools."},{"issue":"1","key":"11370_CR6","doi-asserted-by":"publisher","first-page":"109","DOI":"10.3758\/BRM.40.1.109","volume":"40","author":"M Calvo","year":"2008","unstructured":"Calvo, M., & Lundqvist, D. (2008). Facial expressions of emotion (KDEF): Identification under different display-duration conditions. Behavior Research Methods, 40(1), 109\u2013115. https:\/\/doi.org\/10.3758\/BRM.40.1.109.","journal-title":"Behavior Research Methods"},{"key":"11370_CR7","unstructured":"Chollet, F. (2015). Keras: the python deep learning library. https:\/\/keras.io. Accessed 20 March 2021."},{"key":"11370_CR8","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv:1610.02357.","DOI":"10.1109\/CVPR.2017.195"},{"key":"11370_CR9","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12, 2121\u20132159.","journal-title":"Journal of Machine Learning Research"},{"key":"11370_CR10","doi-asserted-by":"publisher","unstructured":"Ekman, P., & Friesen, W. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2). https:\/\/doi.org\/10.1037\/h0030377.","DOI":"10.1037\/h0030377"},{"issue":"8","key":"11370_CR11","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5120\/ijca2017913009","volume":"159","author":"H Ellaban","year":"2017","unstructured":"Ellaban, H., & Elsaeed, E. (2017). A real-time system for facial expression recognition using support vector machines and k-nearest neighbor classifier. International Journal of Computer Applications, 159(8), 23\u201329. https:\/\/doi.org\/10.5120\/ijca2017913009.","journal-title":"International Journal of Computer Applications"},{"issue":"1","key":"11370_CR12","doi-asserted-by":"publisher","first-page":"012031","DOI":"10.1088\/1757-899X\/705\/1\/012031","volume":"705","author":"S Eng","year":"2019","unstructured":"Eng, S., Ali, H., Cheah, A., & Chong, Y. (2019). Facial expression recognition in JAFFE and KDEF datasets using histogram of oriented gradients and support vector machine. IOP Conference Series: Materials Science and Engineering, 705(1), 012031. https:\/\/doi.org\/10.1088\/1757-899x\/705\/1\/012031.","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"11370_CR13","doi-asserted-by":"publisher","unstructured":"Hamester, D., Barros, P., & Wermter, S. (2015). Face expression recognition with a 2-channel convolutional neural network. In Proceedings of 2015 international joint conference on neural networks (ijcnn). https:\/\/doi.org\/10.1109\/IJCNN.2015.7280539(pp. 1\u20138). Killarney, Ireland: IEEE.","DOI":"10.1109\/IJCNN.2015.7280539"},{"key":"11370_CR14","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of 2016 ieee conference on computer vision and pattern recognition (cvpr). https:\/\/doi.org\/10.1109\/CVPR.2016.90 (pp. 770\u2013778). Las Vegas NV, USA: IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"11370_CR15","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s13640-017-0190-5","volume":"2017","author":"R Holder","year":"2017","unstructured":"Holder, R., & Tapamo, J. (2017). Improved gradient local ternary patterns for facial expression recognition. EURASIP Journal on Image and Video Processing, 2017, 42. https:\/\/doi.org\/10.1186\/s13640-017-0190-5.","journal-title":"EURASIP Journal on Image and Video Processing"},{"key":"11370_CR16","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., & et al. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861."},{"key":"11370_CR17","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. (2017). Densely connected convolutional networks. In Proceedings of 2017 IEEE conference on computer vision and pattern recognition (cvpr). https:\/\/doi.org\/10.1109\/CVPR.2017.243 (pp. 2261\u20132269). Honolulu, HI, USA: IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"11370_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.patrec.2018.04.010","volume":"115","author":"N Jain","year":"2018","unstructured":"Jain, N., Kumar, S., Kumar, A., Shamsolmoali, P., & Zareapoor, M. (2018). Hybrid deep neural networks for face emotion recognition. Pattern Recognition Letters, 115, 101\u2013106. https:\/\/doi.org\/10.1016\/j.patrec.2018.04.010.","journal-title":"Pattern Recognition Letters"},{"issue":"7","key":"11370_CR19","doi-asserted-by":"publisher","first-page":"e18697","DOI":"10.2196\/18697","volume":"22","author":"B Jin","year":"2020","unstructured":"Jin, B., Qu, Y., Zhang, L., & Gao, Z. (2020). Diagnosing parkinson disease through facial expression recognition: video analysis. Journal of Medical Internet Research, 22(7), e18697. https:\/\/doi.org\/10.2196\/18697.","journal-title":"Journal of Medical Internet Research"},{"key":"11370_CR20","unstructured":"Kingma, D., & Ba, J. (2014). Adam: a method for stochastic optimization. arXiv:1412.6980v9."},{"key":"11370_CR21","doi-asserted-by":"crossref","unstructured":"Lasri, I., Riadsolh, A., & El belkacemi, M. (2019). Facial emotion recognition of students using convolutional neural network. In Proceedings of the third international conference on intelligent computing in data sciences (icds) (pp. 1\u20136).","DOI":"10.1109\/ICDS47004.2019.8942386"},{"issue":"1","key":"11370_CR22","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s12652-011-0085-8","volume":"3","author":"C Lee","year":"2012","unstructured":"Lee, C., Shih, C., Lai, W., & Lin, P. (2012). An improved boosting algorithm and its application to facial emotion recognition. Journal of Ambient Intelligence and Humanized Computing, 3(1), 11\u201317. https:\/\/doi.org\/10.1007\/s12652-011-0085-8.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"3","key":"11370_CR23","doi-asserted-by":"publisher","first-page":"128","DOI":"10.3390\/info11030128","volume":"11","author":"M Leo","year":"2020","unstructured":"Leo, M., Carcagni, P., Mazzeo, P., Spagnolo, P., Cazzato, D., & Distante, C. (2020). Analysis of facial information for healthcare applications: a survey on computer vision-based approaches. Information, 11(3), 128. https:\/\/doi.org\/10.3390\/info11030128.","journal-title":"Information"},{"key":"11370_CR24","doi-asserted-by":"publisher","first-page":"104","DOI":"10.2197\/ipsjtcva.7.104","volume":"7","author":"C Liew","year":"2015","unstructured":"Liew, C., & Yairi, T. (2015). Facial expression recognition and analysis: A comparison study of feature descriptors. IPSJ Transactions on Computer Vision and Applications, 7, 104\u2013120. https:\/\/doi.org\/10.2197\/ipsjtcva.7.104.","journal-title":"IPSJ Transactions on Computer Vision and Applications"},{"key":"11370_CR25","doi-asserted-by":"publisher","unstructured":"Liu, P., Han, S., Meng, Z., & Tong, Y. (2014). Facial expression recognition via a boosted deep belief network. In Proceedings of 2014 IEEE conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2014.233(pp. 1805\u20131812). Columbus, OH, USA: IEEE.","DOI":"10.1109\/CVPR.2014.233"},{"key":"11370_CR26","doi-asserted-by":"publisher","unstructured":"Lucey, P., Cohn, J., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In Proceedings of 2010 IEEE computer society conference on computer vision and pattern recognition - work- shops (cvpr workshops). https:\/\/doi.org\/10.1109\/CVPRW.2010.5543262 (pp. 94\u2013101). San Francisco, CA, USA: IEEE.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"11370_CR27","doi-asserted-by":"publisher","unstructured":"Lyons, M., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with gabor wavelets. In Proceedings of 3rd IEEE international conference on automatic face and gesture recognition. https:\/\/doi.org\/10.1109\/AFGR.1998.670949(pp. 200\u2013205). Nara, Japan: IEEE.","DOI":"10.1109\/AFGR.1998.670949"},{"issue":"2","key":"11370_CR28","first-page":"372","volume":"27","author":"Y Nesterov","year":"1983","unstructured":"Nesterov, Y. (1983). A method of solving a convex programming problem with convergence rate o(1\/k2). Soviet Mathematics Doklady, 27(2), 372\u2013376.","journal-title":"Soviet Mathematics Doklady"},{"issue":"1","key":"11370_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian, N. (1999). On the momentum term in gradient descent learning algorithms. Neural Networks : The Official Journal of the International Neural Network Society, 12(1), 145\u2013151. https:\/\/doi.org\/10.1016\/S0893-6080(98)00116-6.","journal-title":"Neural Networks : The Official Journal of the International Neural Network Society"},{"issue":"3","key":"11370_CR30","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins, H., & Monro, S. (1951). A stochastic approximation method. Annals of Mathematical Statistics, 22(3), 400\u2013407. https:\/\/doi.org\/10.1214\/aoms\/1177729586.","journal-title":"Annals of Mathematical Statistics"},{"key":"11370_CR31","doi-asserted-by":"publisher","unstructured":"Sari, M., Moussaoui, A., & Hadid, A. (2021). A simple yet effective convolutional neural network model to classify facial expressions. In S. Chikhi, A. Amine, A. Chaoui, D. Saidouni, & M. Kholladi (Eds.) Lecture notes in networks and systems. https:\/\/doi.org\/10.1007\/978-3-030-58861-8\u2216_14, (Vol. 156 pp. 188\u2013202). Springer.","DOI":"10.1007\/978-3-030-58861-8\u2216_14"},{"key":"11370_CR32","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s00530-021-00854-x","volume":"28","author":"J Shen","year":"2022","unstructured":"Shen, J., Yang, H., & Li, J. (2022). Assessing learning engagement based on facial expression recognition in mooc\u2019s scenario. Multimedia Systems, 28, 469\u2013478. https:\/\/doi.org\/10.1007\/s00530-021-00854-x.","journal-title":"Multimedia Systems"},{"key":"11370_CR33","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556."},{"key":"11370_CR34","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & et al. (2015). Going deeper with convolutions. In Proceedings of 2015 IEEE conference on computer vision and pattern recognition (cvpr). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594 (pp. 1\u20139). Boston. MA, USA: IEEE.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11370_CR35","doi-asserted-by":"publisher","unstructured":"Thomas, C., & Jayagopi, D. (2017). Predicting student engagement in classrooms using facial behavioral cues. In Proceedings of the 1st ACM sigchi international workshop on multimodal interaction for education (mie). https:\/\/doi.org\/10.1145\/3139513.3139514 (pp. 33\u201340). Glasgow Scotland, UK: ACM.","DOI":"10.1145\/3139513.3139514"},{"key":"11370_CR36","doi-asserted-by":"publisher","unstructured":"Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition (cvpr). https:\/\/doi.org\/10.1109\/CVPR.2001.990517 (pp. 511\u2013518). Kauai, HI, USA.","DOI":"10.1109\/CVPR.2001.990517"},{"key":"11370_CR37","doi-asserted-by":"publisher","unstructured":"Yin, D., Omar, S., Talip, B., Muklas, A., Norain, N., & Othman, A. (2017). Fusion of face recognition and facial expression detection for authentication: a proposed model. In Proceedings of the 11th international conference on ubiquitous information management and communication (imcom). https:\/\/doi.org\/10.1145\/3022227.3022247(pp. 1\u20138). Beppu, Japan: ACM.","DOI":"10.1145\/3022227.3022247"},{"key":"11370_CR38","unstructured":"Zeiler, D. (2012). Adadelta: an adaptive learning rate method. arXiv:1212.5701."},{"issue":"5","key":"11370_CR39","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1080\/02564602.2015.1017542","volume":"32","author":"X Zhao","year":"2015","unstructured":"Zhao, X., Shi, X., & Zhang, S. (2015). Facial expression recognition via deep learning. IETE Technical Review, 32(5), 347\u2013355. https:\/\/doi.org\/10.1080\/02564602.2015.1017542.","journal-title":"IETE Technical Review"}],"container-title":["Education and Information Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-022-11370-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10639-022-11370-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-022-11370-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T09:16:12Z","timestamp":1681463772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10639-022-11370-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,11]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["11370"],"URL":"https:\/\/doi.org\/10.1007\/s10639-022-11370-4","relation":{},"ISSN":["1360-2357","1573-7608"],"issn-type":[{"value":"1360-2357","type":"print"},{"value":"1573-7608","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,11]]},"assertion":[{"value":"27 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}