This project trains a CNN on the FER-2013 dataset to detect emotions (angry, disgusted, fearful, happy, neutral, sad, surprised) from face images. It uses OpenCV with Haar cascade classifiers for real-time face detection and TensorFlow/Keras for model training and inference. The model achieves approximately 63% test accuracy after 50 epochs and supports real-time webcam-based emotion detection.