Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks Subject The topic of the resource Convolutional neural network; Emotion detection; Facial expression recognition.; Residual networks; Student engagement detection Description An account of the resource Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED &amp; iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure. 2020, Intelligent Engineering &amp; System. All rights reserved. Creator An entity primarily responsible for making the resource Thiruthuvanathan M.M.; Krishnan B.; Rangaswamy M. Source A related resource from which the described resource is derived International Journal of Intelligent Engineering and Systems, Vol-14, No. 2, pp. 236-247. Publisher An entity responsible for making the resource available Intelligent Network and Systems Society Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22266/ijies2021.0430.21" target="_blank" rel="noreferrer noopener">https://doi.org/10.22266/ijies2021.0430.21</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102826027&amp;doi=10.22266%2Fijies2021.0430.21&amp;partnerID=40&amp;md5=e7de08338c059e769a72b0cbf4dcc854" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102826027&amp;doi=10.22266%2fijies2021.0430.21&amp;partnerID=40&amp;md5=e7de08338c059e769a72b0cbf4dcc854</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 2185310X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Rangaswamy M., Department of Psychology, School of Social Sciences, Bangalore, India