Facial Emotion Recognition System is a Deep Learning and Computer Vision project that detects and classifies human emotions from facial expressions in real-time using webcam input.
The system uses TensorFlow, Keras, and MobileNetV2 Transfer Learning to build an efficient emotion classification model. OpenCV is used for real-time face detection and video processing, while image preprocessing and data augmentation techniques improve model generalization and performance.
The model is trained to recognize multiple emotions such as Happy, Sad, Angry, Fear, Surprise, Disgust, and Neutral. Performance evaluation was conducted using metrics like Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.
This project demonstrates the practical application of Artificial Intelligence in areas such as human-computer interaction, mental health monitoring, smart surveillance systems, customer behavior analysis, and emotion-aware applications.