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282094

FER_ML: Facial Emotion Recognition using Machine Learning

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Last updated: 24 Dec 2024

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Abstract

Recently, facial recognition has been one of the most crucial technologies people need. Facial recognition has attracted a lot of the crowd; for example, it has been used in security on most modern devices. Using machine and deep learning, overall performance will be improved, and the identification accuracy will be more precise. We aim to discover how well these algorithms perform in classifying human facial expressions and whether or not we can depend on them. The steps are as follows. First, we embed the images from the dataset, then split the dataset into 70% training data and 30% testing data; after that, we apply five different algorithms: Support Vector Machine, K-nearest Neighbor, Logistic Regression, Naive Bayes, and Random Forest. Support Vector Machine achieved an accuracy rate of 36%, K-nearest Neighbor achieved an accuracy rate of 52.3%, Logistic regression achieved an accuracy rate of 64.2%, and Naive Bayes achieved an accuracy rate of 38.1%. Random Forest achieved an accuracy rate of 51.7%. The dataset used was a cleaned version of the FER13 dataset, which contains 16,780 images divided into five classes (angry, happy, neutral, disgust, and fear). The results show that Logistic Regression proved to be the most accurate classifier among the presented ones, with an F1-Score of 63.8% and an accuracy of 64.2%.

DOI

10.21608/jocc.2023.282094

Keywords

support vector machine (SVM), Naive Bayes(NB) K-nearest Neighbor (KNN), Logistic Regression (LR) Random Forest (RF) Machine Learning Facial Emotion Recognition

Authors

First Name

Diaa

Last Name

AbdElminaam

MiddleName

s

Affiliation

Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Email

diaa.salama@miuegypt.edu.eg

City

-

Orcid

0000-0002-1544-9906

First Name

shihab

Last Name

Mostafa

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

shihab1908443@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

Bilal

Last Name

Tamer Ghareeb

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University

Email

bilal1908384@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

FAdy

Last Name

Tarek

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

fady1900456@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

HAshim

Last Name

Said

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

hashim1913453@miuegypt.edu.eg

City

cairo

Orcid

-

Volume

2

Article Issue

1

Related Issue

39171

Issue Date

2023-01-01

Receive Date

2022-10-16

Publish Date

2023-01-25

Page Start

40

Page End

49

Online ISSN

2636-3577

Article File

JOCC_Volume 2_Issue 1_Pages 40-49.pdf

PDF . 493.4kB

Link

https://jocc.journals.ekb.eg/article_282094.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=282094

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5

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

FER_ML: Facial Emotion Recognition using Machine Learning

Details

Type

Article

Created At

24 Dec 2024