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Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorKhalid Bhatti, Yusra
dc.contributor.authorJamil, Afshan
dc.contributor.authorNida, Nudrat
dc.contributor.authorHaroon Yousaf, Muhammad
dc.contributor.authorViriri, Serestina
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.date.accessioned2021-06-02T09:59:23Z
dc.date.available2021-06-02T09:59:23Z
dc.date.issued2021-05-03
dc.description.abstractClassroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s 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’s 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-level 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’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.en
dc.format.extent17
dc.identifier.bibliographicCitationBhatti, Y. K., Jamil, A., Nida, N., Yousaf, M. H., Viriri, S., & Velastin, S. A. (2021). Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine. Computational Intelligence and Neuroscience, 2021, 1–17.es
dc.identifier.doihttps://doi.org/10.1155/2021/5570870
dc.identifier.issn1687-5265
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue5570870
dc.identifier.publicationlastpage17
dc.identifier.publicationtitleComputational Intelligence and Neuroscienceen
dc.identifier.publicationvolume2021
dc.identifier.urihttps://hdl.handle.net/10016/32823
dc.identifier.uxxiAR/0000027952
dc.language.isoeng
dc.publisherHindawien
dc.rights© 2021 Yusra Khalid Bhatti et al.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.titleFacial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machineen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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