FACIAL EMOTION DETECTION USING DEEP LEARNING

Built and trained a deep learning model using the FER-2013 dataset for emotion classification, implementing MobileNetV2 with fine-tuning for 7 emotions (angry, happy, sad, disgust, fear, neutral), achieving 81% accuracy after 12 epochs.Preprocessed the dataset by resizing, normalizing, and labeling images, and used Haarcascade frontal face detection for real-time facial detection and emotion prediction.Implemented real-time emotion classification using TensorFlow, Keras, and OpenCV,processing webcam feed to predict emotions live