Pelios—Emotion Detection Using Machine Learning in Real Time

Emotion detection involves identifying and analyzing human emotions and their expressions through a variety of sources, including facial expressions, body language, speech patterns, and other nonverbal signals. As a subset of affective computing, it aims to develop technology that recognizes and responds to human emotions. Emotion detection technology uses various techniques, such as machine learning and deep learning, to analyze the data and classify it into different emotions, such as happiness, sadness, anger, fear, and disgust. There is a wide range of applications for the technology, including market research, customer service, healthcare, and education. The basic utilization of our model lies in the fact that it can detect these emotions in a fast and efficient manner using OpenCV architecture and deep learning concepts. The model will take access to the Webcam or any device that can capture photos or videos in real-time. When an individual or a group of people come in front of the webcam/camera, then the machine will detect the face, recognize the emotion, and display the result on our screen. In this paper, we also have the Haar cascade algorithm and convolution neural network (CNN) to perform the task of emotion detection. In this work, we show that the proposed methodology can identify human emotions with human faces having spectacles as well as without spectacles.