MoodMapper: Facial Emotion Recognition

ABSTRACT This paper presents 'Mood Mapper', a real-time facial emotion recognition system developed using deep learning techniques. It aims to detect and classify human emotions from facial expressions using a Convolutional Neural Network (CNN). The system is trained on FER-2013 dataset and supports emotion detection through image input or live webcam feed. Preprocessing steps like grayscale conversion, resizing, and normalization are applied for improved accuracy. The proposed system recognizes seven basic emotions and is implemented using Python, TensorFlow, Keras, and OpenCV. It demonstrates significant potential for real-world applications such as mental health monitoring, education, and customer engagement.