Machine Learning-Based Real-Time Emotion Detection System For Employees

Abstract

These days, companies care more about their workers' health and happiness than anything else. Just because it will
have an impact on how productive an employee is and how much they contribute to the team. A fascinating and
busy field of study for the last few decades has been the automated interpretation of facial expressions using
machine learning. To automatically identify employees' emotions in real time using machine learning, this research
proposes the Real time Employee Emotion Detection System (RtEED). Through the RtEED system, employers are
able to monitor their workers' emotional health and communicate with them through messages when any distress
becomes apparent. As a result, workers will be able to make more informed choices, increase their focus at work,
and embrace healthier lifestyles that lead to greater productivity. In order to train a machine learning model, CMU
Multi-PIE Face Data is used. A camera will be provided to every employee so that any expressions on their face
may be captured in real-time. Based on the picture, the RtEED algorithm can determine whether the subject is
happy, sad, surprised, afraid, disgusted, or angry. We have accomplished what we set out to do. Emotion
recognition, machine learning, and facial expression analysis are some of the keywords related to artificial
intelligence.