Multi-modal Emotion Detection System

Facial recognition-based emotion detection is a growing research area that combines artificial intelligence and psychology to analyze human emotions through facial expressions. By examining features like eye movement, mouth shape, and eyebrow positioning, these systems identify emotions such as happiness, sadness, anger, fear, and surprise. Modern approaches employ advanced algorithms, particularly Convolutional Neural Networks (CNNs), alongside datasets like FER-2013 and AffectNet, achieving impressive accuracy. Applications range from mental health assessment to customer sentiment analysis and adaptive learning. However, challenges like real-time processing, cultural variations in emotional expression, and dataset biases remain. This paper reviews current methods, highlights existing limitations, and suggests future directions for creating more robust, inclusive emotion detection systems.