Deep Learning–Based Facial Expression Recognition for Emotion Understanding in Human–Computer Interaction

Abstract

Facial expression recognition (FER) has become an important field of study in computer vision and artificial intelligence because it could help machines understand how people feel. FER systems try to figure out how people are feeling by looking at changes in their eyes, mouth, eyebrows, and other parts of their face. They can tell if someone is happy, sad, angry, scared, surprised, or disgusted. Recent advancements in machine learning and deep neural networks have significantly improved the accuracy and robustness of emotion recognition models, enabling their deployment across diverse real-world applications. These applications encompass various fields, including mental health diagnostics, where FER can aid in the evaluation and treatment of psychological disorders such as depression and anxiety; education, where emotion-aware systems can promote adaptive and individualized learning environments; and human-computer interaction, where emotionally responsive interfaces improve user engagement and experience. Even though there has been a lot of development, there are still several problems that need to be solved. These include being able to reliably detect subtle and complex emotional indicators, being able to work well in different lighting conditions and when there are obstructions, and the ethical issues that come up when collecting and using facial data. To make sure that FER technologies are used safely and effectively in real-world systems, these problems need to be solved.