Advances in Facial Emotion Recognition: Leveraging Machine Learning for Human Emotion Analysis

A rapidly developing field called facial expression detection holds enormous promise in a variety of industries, in-cluding marketing, healthcare, and human-computer interaction. This research provides a thorough analysis of machine learning methods for this job based on neural networks. We explore the application of deep neural networks, such as CNNs and RNNs, emphasising the value of reliable training data. We compare model performance to benchmarks while investigating elements like precision and computational complexity. Also included are ethical issues, privacy worries, and appropriate deployment tactics. Our test results demonstrate how well these models perform at identifying facial expressions of emotion, as well as providing information about their current limitations and potential future study areas.