Performance analysis of emotion detection using machine learning algorithms

This project’s goal is not just to identify emotions, but also to develop methods for training and evaluating how well various machine learning models recognize emotions. This study investigates how different facial emotions (happy, fear, neutral, sad, angry, astonished) may be recognized using feature extraction from face expressions in conjunction with a neural network combination. Convolutional neural networks, decision trees, logistic regression, and K-Nearest Neighbors will be among the several models investigated. The trial results shown that the recommended model is more accurate than 80% at identifying emotions.