Abstract

This paper presents a Human Emotion Detection system utilizing Convolutional Neural Networks (CNN). The model is trained on facial expression data to classify various human emotions such as happiness, sadness, anger, and surprise. The CNN approach allows the system to automatically learn features that distinguish different emotions. We describe the model architecture, data preprocessing, and training process in detail. Key results demonstrate the system's high accuracy in detecting emotions in real-time applications. This work highlights the potential of CNNs in emotion recognition and its implications for applications in human-computer interaction, security, and behavioral analysis.