This project focuses on developing a facial expression recognition system utilizing the Expression in-the-Wild (ExpW) dataset. The dataset consists of 91,793 face images labeled across seven fundamental expression categories: angry, disgust, fear, happy, sad, surprise, and neutral. The primary goal is to create a robust model capable of accurately identifying human emotions in real-world scenarios.
- Dataset: The Expression in-the-Wild (ExpW) dataset, which provides a rich variety of facial expressions.
- Model Training: The model is trained using a deep learning approach over 10 epochs, achieving:
- Training Accuracy: 98%
- Validation Accuracy: 92%
- Test Accuracy: 91%
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Hyperparameter Tuning: Key hyperparameters like learning rate and batch size were optimized using grid search to enhance model performance.
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Data Division:
- Training Set: 70%
- Validation Set: 10%
- Test Set: 20%
This division ensures robust model evaluation and performance assessment.
The project utilizes Python and popular libraries such as TensorFlow/Keras for model development and training. The architecture is designed to efficiently learn and generalize from the dataset, providing a reliable tool for recognizing facial expressions.