Emotion Detection using Image Processing

Authors

  • Siddiqa Saniya Student, School of Computing and Information Technology, REVA University, Bengaluru, India
  • Mallikarjun M. Kodabagi Deputy Director, School of Computing and Information Technology, REVA University, Bengaluru, India

Keywords:

emotion detection, image processing, deep learning, mobilenet, transfer learning, early stopping, model checkpointing

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.

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Published

27-05-2023

Issue

Section

Articles

How to Cite

[1]
S. Saniya and M. M. Kodabagi, “Emotion Detection using Image Processing”, IJRESM, vol. 6, no. 5, pp. 125–127, May 2023, Accessed: Dec. 30, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2710