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Extensive research has been done on the analysis of student\u2019s facial expressions, but the impact of instructor\u2019s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher\u2019s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor\u2019s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high\u2010level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor\u2019s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn\u2013Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state\u2010of\u2010the\u2010art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, <jats:italic>F<\/jats:italic>1\u2010score, and recall.<\/jats:p>","DOI":"10.1155\/2021\/5570870","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T18:53:53Z","timestamp":1620068033000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2876-8460","authenticated-orcid":false,"given":"Yusra Khalid","family":"Bhatti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2256-1277","authenticated-orcid":false,"given":"Afshan","family":"Jamil","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-1413","authenticated-orcid":false,"given":"Nudrat","family":"Nida","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8255-1145","authenticated-orcid":false,"given":"Muhammad Haroon","family":"Yousaf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2850-8645","authenticated-orcid":false,"given":"Serestina","family":"Viriri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6775-7137","authenticated-orcid":false,"given":"Sergio A.","family":"Velastin","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"Applications of Human-Computer Interaction and Robotics Based on Artificial Intelligence","author":"Silva D. 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