At this time, the safety and well-being of workers is the top priority in the workplace. because it will reduce an employee's efficiency and effectiveness on the job, thereby diminishing their value to the team. In conclusion, autonomous Utilizing machine learning for the purpose of analyzing facial expressions is a fascinating and thriving field of study throughout the past few decades. Using machine learning, the Real time Employee Emotion Detection System (RtEED) can instantly determine how an employee is feeling. The RtEED technology enables management to monitor worker morale and relay any detected emotions to the appropriate staff member. As a result, workers will be able to make more informed decisions, focus more intently on their tasks at hand, improve their overall health, and increase their productivity. The FER-2013 dataset was used to teach the A model built upon machine learning techniques. Every worker A webcam will be integrated as a feature that can to seize or record their current expressions. Using the image that was captured, the RtEED system can detect and categorise six distinct emotions, including joy, sorrow, surprise, fear, disgust, and rage. The outcomes confirm that the objectives were successfully completed.

Emotion detection, Convolution Neural Network, Machine Learning, Support Vector Machine