CNN BASED EMOTION MAPPING SYSTEM

Emotion recognition through facial expressions is a significant application of deep learning, enabling machines tointerpret human emotions with high accuracy. This project proposes a Convolutional Neural Network (CNN)-basedmodel for automated facial emotion detection using the FER-2013 dataset, which comprises 35,887 labeledgrayscale facial images categorized into seven key emotions: anger, disgust, fear, happiness, sadness, surprise, andneutral.The CNN model is designed to extract intricate facial features and classify emotions efficiently. Advancedpreprocessing techniques such as image normalization, augmentation, and noise reduction enhance modelperformance. The architecture is optimized using multiple convolutional layers, activation functions, and poolingoperations to ensure precise feature extraction. The model is trained and validated using deep learning frameworks,achieving high classification accuracy through fine-tuned hyperparameters.This study highlights the importance of CNNs in emotion recognition, demonstrating their potential for real-worldapplications in human-computer interaction, mental health assessment, security, and customer experience analysis.The research further explores challenges such as data imbalance, real-time deployment, and accuracy enhancement,providing insights for future advancements in deep learning-based emotion detection