
Editorial
CNN Based Face Emotion Recognition System for Healthcare Application
@ARTICLE{10.4108/eetpht.10.5458, author={R Kishore Kanna and Bhawani Sankar Panigrahi and Susanta Kumar Sahoo and Anugu Rohith Reddy and Yugandhar Manchala and Nirmal Keshari Swain}, title={CNN Based Face Emotion Recognition System for Healthcare Application}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={CNN, BCI, Emotions, ML}, doi={10.4108/eetpht.10.5458} }
- R Kishore Kanna
Bhawani Sankar Panigrahi
Susanta Kumar Sahoo
Anugu Rohith Reddy
Yugandhar Manchala
Nirmal Keshari Swain
Year: 2024
CNN Based Face Emotion Recognition System for Healthcare Application
PHAT
EAI
DOI: 10.4108/eetpht.10.5458
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.
Copyright © 2024 R. Kishore Kanna et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.