Facial Emotion Recognition using Convolutional Neural Network

Authors

  • Mahima A. Shejwadkar Student, Department of Information Science and Engineering, Srinivas Institute of Technology, Mangaluru, India
  • Sowmya Assistant Professor, Department of Information Science and Engineering, Srinivas Institute of Technology, Mangaluru, India
  • Cindrella M. D. Souza Student, Department of Information Science and Engineering, Srinivas Institute of Technology, Mangaluru, India
  • V. B. Mayuri Raj Student, Department of Information Science and Engineering, Srinivas Institute of Technology, Mangaluru, India
  • Viviana Joseph Fernandes Student, Department of Information Science and Engineering, Srinivas Institute of Technology, Mangaluru, India

Keywords:

Autistic, Convolutional Neural Network (CNN), Django, Keras, Machine Learning (ML)

Abstract

Human Facial Emotions fetch much of information visually rather than articulately. Facial Emotion Recognition plays a significant role in human-machine interaction. Automatic facial emotion recognition system has multiple applications, but not limited to human behavior understanding, synthetic human expressions and detection of mental disorder. Recognizing emotions high recognition rate by computer is a very challenging task to perform. The two very popular methods proposed to use mostly in automatic FER systems are based on the geometry and appearance of the structure. Facial Emotion Recognition is mainly performed in 4 stages, which are pre-processing, phase detection, feature extraction and expression classification.

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Published

25-07-2021

Issue

Vol. 4 No. 7 (2021)

Section

Articles

License

Copyright (c) 2021 Mahima A. Shejwadkar, Sowmya, Cindrella M. D. Souza, V. B. Mayuri Raj, Viviana Joseph Fernandes

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. A. Shejwadkar, Sowmya, C. M. D. Souza, V. B. M. Raj, and V. J. Fernandes, “Facial Emotion Recognition using Convolutional Neural Network”, IJRESM, vol. 4, no. 7, pp. 288–290, Jul. 2021, Accessed: Apr. 25, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/1073