Face Detection to Recognize Students’ Emotion and Their Engagement: A Systematic Review
Benyoussef Abdellaoui, Aniss Moumen, Younès El Bouzekri El Idrissi, Ahmed Remaida
Abstract
Following the Coronavirus COVID pandemic 19, many countries have adopted distance education to ensure pedagogical continuity for which they have not been ready yet to deal with. So, to reduce the spread of the virus, since March 2020, Morocco has called for state health emergency and imposed strict confinement on the population. In response to this, all training establishments have been locked down. In such a context of crisis and disability, teachers, students, and their families have found themselves in a delicate situation to manage, where new distance learning needs and practical training have been imposed without adequate measures for conducting an education as an integral part of a normative academic curriculum. In this article we will discuss the teachers' inability to asses the students' emotional state and determine their engagement which can be clearly read from their faces in normal teaching situation. It aims at analyzing and responding to problems related to the detection of faces to automatically deduce emotions and study student engagement while exploring and analyzing the references available on the some databases cited in the article. This literature review is supplemented by thematic analysis and a meta-analysis of the corpus. This study has identified both quantitative and qualitative and/or experimental work.