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phat 24(1):

Editorial

CNN Based Face Emotion Recognition System for Healthcare Application

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  • @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
R Kishore Kanna1,*, Bhawani Sankar Panigrahi2, Susanta Kumar Sahoo3, Anugu Rohith Reddy2, Yugandhar Manchala2, Nirmal Keshari Swain2
  • 1: Jerusalem College of Engineering
  • 2: Vardhaman College of Engineering
  • 3: Indira Gandhi Institute of Technology
*Contact email: kishorekanna007@gmail.com

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.

Keywords
CNN, BCI, Emotions, ML
Received
2023-12-21
Accepted
2024-03-10
Published
2024-03-18
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5458

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.

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