Understanding Human Emotions and Detecting Stress Levels using YOLO

Abstract

Abstract: The emotion recognition system can detect the emotions of a person through their facial expression. Detected emotions can fall into any of the six main data of emotions can fall into any of the six main data of emotions such as happiness, sadness, fear, surprise, disgust, and anger. It is the process of using datasets in recognizing the emotion detected by the system through image processing. Emotion detection leads to maintain a healthy environment in lives. We will use the deep learning technology to generate models with emotion based facial expression to recognized emotions. The datasets contain a large number of labelled facial images or videos with different emotions and poses, enabling researchers to train and test their models effectively. To detect the emotions we have used the YOLOV3 for implementing the existing system and YOLOV7 is used to implement the proposed system. We know that facial expression is one of the most powerful, natural and universal signals for human to convey their emotions states and intentions. Numerous studies have been conducted on automatic facial expression analysis because of its practical important in sociable robotics, medical treatment, driver fatigue surveillance, and many other human-computer interaction systems. In the field of computer vision and machine learning various facial expression recognition (FER) systems have been explored to encode expression information from facial representation. As early as the 21th Ekman and Frisen defined six basic emotions based on cross-culture study, which indicated that humans perceive certain basic emotions in the same way regardless of culture. These prototypical facial expressions are anger, disgust, fear, happiness, sadness, and surprise. Recently advanced research on neuroscience and psychology argued that the model of six basic emotions are culture-specific and not universal.