Group-Face-Emotion-Recognition

For the face emotion recognition project, we have implemented a Convolutional Neural Network (CNN) model using Keras and OpenCV libraries to detect and recognize facial expressions in real-time from webcam or video input. The goal of the project is to accurately classify facial expressions into seven emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise. Model: Convolutional Neural Network (CNN) Training Dataset Size: 28,821 images (across seven emotion categories) Testing Dataset Size: 7,066 images (across seven emotion categories) Image Size: 48x48 pixels (grayscale) Epochs: 100 Training Accuracy: [98] Testing Accuracy: [96]