Advancements and Challenges in Emotional Analysis Using Machine Learning: A Comprehensive Review of Facial Expression Recognition Models

The field of emotional analysis using computer vision is one of the most important fields in artificial intelligence. The network has potential for many exciting areas in humancomputer interaction, health, and entertainment. This paper delves into different machine learning techniques applied to recognizing and interpreting emotions from facial expressions. It includes Support Vector Machines, Convolutional Neural Networks, Generative Adversarial Networks, Capsule Networks, and Vision Transformers. Although important advances have taken place, these models still have a few limitations: they struggle to generalize across different datasets; deal with occlusions and tilted faces; and may be computationally complex. In addition, these models do not offer interoperability and may have possible cultural biases. Also, the analysis underlines problems of recognition of microexpressions. Therefore, ways of overcoming such limitations must be targeted in the future development of more reliable and precise Emotion Recognition Systems. This paper aims to present current methodologies with an in-depth analysis of their effectiveness.