This project presents an AI-powered facial emotion recognition system designed to analyze human facial expressions from still images and accurately predict emotional states using deep learning techniques. The system leverages Convolutional Neural Networks (CNNs) to identify and classify facial emotions with high accuracy, providing a confidence score for each prediction. # Supported Emotion Classes The model is trained to classify the following emotions: * Happiness * Sadness * Anger * Surprise * Fear * Neutral * Disgust # How the System Works 1. Face Detection– Automatically detects faces within the input image. 2. Image Preprocessing – The detected face is processed using: * Grayscale conversion * Image resizing * Pixel normalization 3. Emotion Prediction – The processed image is passed through a trained CNN model to classify the emotion. 4. Confidence Scoring – The system outputs the predicted emotion along with a probability/confidence score. # Key Features * The system was designed and implemented by me to analyze still images only, without requiring a camera or live streaming input. * Automatic face detection within images * Deep learning-based emotion classification using CNN * Provides confidence level for each prediction * Supports batch processing of multiple images * Clean, modular, and scalable Python implementation # Potential Applications * Customer emotion analysis for marketing insights * User behavior analysis and sentiment research * Human–Computer Interaction (HCI) systems * Intelligent image analysis platforms * AI-powered analytics tools for businesses # Technologies Used * Python * Deep Learning (CNN) * TensorFlow / Keras or PyTorch * OpenCV for face detection * NumPy & data preprocessing tools