This project utilizes Convolutional Neural Networks (CNN) for facial emotion detection, enabling real-time analysis of emotions through a laptop's camera. Leveraging CNN layers, the model is trained on a Kaggle dataset, enabling it to accurately detect facial expressions with an average accuracy ranging between 70-72 percent.
Features:
- CNN Model: Utilizes deep learning techniques, specifically CNN layers, for robust facial emotion recognition.
- Real-time Detection: Employs OpenCV to detect faces in real-time using the laptop's camera, allowing immediate analysis of facial expressions.
- Kaggle Dataset: Trained on a comprehensive Kaggle dataset, ensuring diverse and extensive training data for accurate emotion recognition.
Applications:
- Healthcare
- Human-Computer Interaction (HCI)
- Security and Surveillance
- DDA(Dynamic Difficulty Adjustment) games