Emotion Detection using Image Processing

Abstract

Emotion detection using image processing is a technique that involves recognizing human emotions from facial expressions by analyzing images. This project focuses on developing an emotion detection model using deep learning techniques on a dataset of labeled images. The dataset is preprocessed and augmented to improve performance, and a MobileNet model is used as the base model for transfer learning. The model is trained using an Adam optimizer and categorical cross-entropy loss function, and accuracy is evaluated using a validation set. The project also includes early stopping and model checkpointing techniques to improve model performance and save the best model. The results of the project show that the model can accurately detect emotions from facial expressions in real-time. This technique has a wide range of applications in fields such as healthcare, security, and entertainment, and has the potential to significantly improve human-machine interaction.