In this project, we developed an emotion detection system using Convolutional Neural Networks (CNN) to accurately classify human emotions from facial expressions. Leveraging a dataset of labeled facial images, we trained a CNN model to recognize and categorize emotions such as happiness, sadness, anger, and surprise. The model architecture includes multiple convolutional layers for feature extraction, followed by fully connected layers for classification. This approach enables real-time emotion detection with high accuracy, making it suitable for applications in human-computer interaction, mental health monitoring, and customer service enhancement.
