CNN Based Face Emotion Recognition System for Healthcare Application

Authors

  • R Kishore Kanna Jerusalem College of Engineering
  • Bhawani Sankar Panigrahi Vardhaman College of Engineering image/svg+xml
  • Susanta Kumar Sahoo Indira Gandhi Institute of Technology image/svg+xml
  • Anugu Rohith Reddy Vardhaman College of Engineering image/svg+xml
  • Yugandhar Manchala Vardhaman College of Engineering image/svg+xml
  • Nirmal Keshari Swain Vardhaman College of Engineering image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.10.5458

Keywords:

CNN, BCI, Emotions, ML

Abstract

INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately.

OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases.

RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios.

CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.

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References

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Published

18-03-2024

How to Cite

1.
Kishore Kanna R, Panigrahi BS, Sahoo SK, Reddy AR, Manchala Y, Swain NK. CNN Based Face Emotion Recognition System for Healthcare Application. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 18 [cited 2024 Apr. 21];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5458