EmoTract: A Comprehensive Approach for Detecting Learner's Emotions in Real-Time in Virtual Environments using Deep Learning

It necessitates real-time emotion detection to monitor the need for remote interviews and virtual classrooms. EmoTract, a CNN-and-ResNet-50-powered tool, detects facial expressions, and thereby observes the dominant emotional states such as engagement, disengagement, and confusion. This instant feedback provided allows for better student participation and for educators or interviewers to adapt their methods appropriately. The technical implementation of EmoTract, the role of emotional intelligence in virtual interactions, and the broader societal impact are discussed with a focus on its potential for creating empathetic and interactive digital learning and communication environments. EmoTract uses CNN and ResNet-50 for facial expression analysis and detection of key emotions, such as engagement, disengagement, or confusion, in real-time for enhancing remote interviews and virtual classrooms. With the immediate provision of emotional feed, it helps teachers and interviewers in giving necessary adjustments to their method which would help people engage and learn better. This paper explores EmoTract's technical design, the role of emotional intelligence in digital communications, and the way it will transform virtual communication through the organic incorporation of emotional awareness into the digital environment.