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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM33503

Facial Emotion Recognition using Convolutional Neural Network

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM33503

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM33503

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MNS

Mohammed Nashat Sadat

M

M.Shashank

S

S.Nandini

G

G.Nandini

M

M.Nandini

DDT

Dr. D. Thiyagarajan

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Abstract

Emotion is an expression that human use in expressing their feelings. It can be express through facial expression, body language and voice tone. Humans’ facial expression is a major way in conveying emotion since it is the most powerful, natural and universal signal to express humans’ emotion condition. However, humans’ facial expression has similar patterns, and it is very confusing in recognizing the expression using naked eye. For instance, afraid and surprised is very similar to one another. Thus, this will lead to confusion in determining the facial expression. Hence, this study aims to develop a application for emotion recognition that can recognize emotion based on facial expression in real-time. The Deep Learning based technique, Convolutional Neural Network (CNN) is implemented in this study. The Mobile Net algorithm is deployed to train the model for recognition. There are four types of facial expressions to be recognized which are happy, sad, surprise, and disgusting.

KEYWORDS

Facial Emotion Recognition Deep Learning CNN Image Processing.

Cite This Article

Mohammed Nashat Sadat, M.Shashank, S.Nandini, G.Nandini, M.Nandini, Dr. D. Thiyagarajan, "Facial Emotion Recognition using Convolutional Neural Network," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM33503

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Publication Date

12/31/45420

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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