Human Emotion Identification Based on Facial Expression using Image Processing

Abstract

The field of using image processing for automated human emotion identification based on facial expression is one that is rapidly growing and has many potential uses. There may be many advantages to understanding human emotions, such as better human-computer interaction and more accurate diagnosis of mental illness. Understanding emotions can help you communicate more effectively with others. Traditional methods of emotion recognition rely on human interpretation, which may be expensive, time-consuming, and prone to error. Recently though, advances in computer vision and machine learning have made it possible to design fully automated systems for identifying emotions based on facial expressions. These algorithms are competent in this area because of their analysis of facial expressions. This research provides a systematic review of previous work on emotion analysis from photographs of people's faces. Issues, boundaries, and potential directions for further study are all discussed. We also explore the many suggested methods for emotion recognition and provide a thorough evaluation of the existing datasets. The applications of AI-based emotion detection are also discussed, along with the need for objective standards of evaluation and verification of the results. Our research leads us to the conclusion that automated emotion recognition algorithms have great potential as a tool for enhancing human-computer interaction, mental health assessment, and other applications, but that more work needs to be done to improve their accuracy and robustness.