This project aims to develop a facial expression recognition system using convolutional neural networks (CNNs) to accurately detect and classify emotions from facial images. By leveraging deep learning techniques, the system analyzes facial features and expressions to classify emotions such as happiness, sadness, anger, surprise, fear, disgust, and neutrality. The trained CNN model is capable of robustly identifying emotional states from facial images in real-time, facilitating applications in various domains, including entertainment, market research, education, healthcare, human-computer interaction, gaming, retail, and automotive sectors. Through its ability to interpret human emotions, the system enhances user experiences, improves service quality, and enables innovative applications across diverse industries.
- Accurate detection and classification of human emotions from facial images.
- Real-time emotion detection using a trained CNN model.
- Applications in entertainment, market research, education, healthcare, human-computer interaction, gaming, retail, and automotive sectors.
- Enhances user experiences and service quality through emotion-aware technology.
- Enables innovative applications and services across various industries.
- Clone the repository: git clone https://github.com/MuhamadBarhan/facial-expression-recognition-using-cnn.git
- Install the required dependencies
- Prepare the dataset of labeled facial images representing different emotions.
- Train the CNN model using the provided scripts or notebook.
- Evaluate the trained model on a separate validation dataset to assess performance metrics.
- Deploy the trained model for real-time facial expression recognition in your desired application or environment.
Contributions to this project are welcome. You can contribute by:
- Reporting issues or bugs
- Suggesting new features or improvements
- Submitting pull requests to address open issues or add new features
This project is licensed under the [MIT License]LICENSE.
