Emotion Detection System using Machine Learning

Abstract - One of the most crucial factors to consider in any project pertaining to affective computing nowadays is Emotion detection. The corporate sector has seen the development of Emotion detection systems as a lucrative potential due to the nearly infinite uses of this emerging field. In recent years, a large number of start-up businesses have appeared that are nearly solely focused on a particular kind of Emotion detection technology. We provide a comprehensive overview of the state-of-the-art human Emotion detection systems in this study. In order to achieve this, we examine the various sources from which emotions can be deduced as well as the technology now in use for doing so. We also examine a few areas of application where this technology has been used. This survey has enabled us to pinpoint the advantages of integrating computer vision and deep learning for real-time human-computer interaction (HCI). This paper details the implementation of a real-time emotion detection system using a webcam, combining the Haar Cascade Classifier for efficient face localization and a Deep Facebased Convolutional Neural Network (CNN) for classifying seven universal emotions, demonstrating a core accuracy of sim 66% on complex video feeds.