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A Comparative Study of Different Face Shape Classification Techniques

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Last updated: 05 Jan 2025

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Tags

Machine Learning
Computer Vision
Face Shapes
Classification
SVM
A Comparative Study of Different Face Shape Classification Techniques
2021 International Conference on Electronic Engineering (ICEEM)

Abstract

Throughout the last years, there has been an increasing interest in developing useful computer vision techniques that help in many fields. Face shape classification is considered a common task in beauty and fashion purposes. The aim of this paper is to represent a comparative study of different supervised learning algorithms used in face shape classification. The classification was based on extracted facial features for the 5 different face shapes: Heart, Square, Long, Oval and Round as labels. Different classification algorithms that use landmark distance ratios and angles as features were compared: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multilayer Perception (MLP), Random Forest (RF), AdaBoost, and Naïve Bayes. Around five thousand female celebrities' images were used for training and testing the different classifiers. The results showed that the SVM classifier with radial basis function kernel achieved the highest overall accuracy of 82%.

Keywords

Machine Learning, Computer Vision, Face Shapes, Classification, SVM

Authors

First Name

Mohamed

Last Name

Hossam

Affiliation

Undergraduate Students, School of Engineering and Applied Sciences, Nile University

Email

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City

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Orcid

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First Name

Ahmed

Last Name

Afify

Affiliation

Undergraduate Students, School of Engineering and Applied Sciences, Nile University

Email

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City

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Orcid

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First Name

Mohamed

Last Name

Rady

Affiliation

Undergraduate Students, School of Engineering and Applied Sciences, Nile University

Email

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City

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Orcid

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First Name

Michael

Last Name

Nabil

Affiliation

Undergraduate Students, School of Engineering and Applied Sciences, Nile University

Email

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City

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Orcid

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First Name

Kareem

Last Name

Moussa

Affiliation

Undergraduate Students, School of Engineering and Applied Sciences, Nile University

Email

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City

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Orcid

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First Name

Retaj

Last Name

Yousri

Affiliation

Wireless Intelligent Networks Center (WINC), Nile University

Email

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City

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Orcid

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First Name

Mohamed

Last Name

Darweesh

Affiliation

Electronics and Computer Engineering, School of Engineering and Applied Sciences, Nile University, Giza, Egypt

Email

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City

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Orcid

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Volume

2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021

Issue Date

1 Jan 2021

Publish Date

14 Jun 2021

Page Start

89

Page End

94

Link

https://iceem2021.conferences.ekb.eg/article_1138.html

Order

16

Publication Type

Conference

Publication Title

2021 International Conference on Electronic Engineering (ICEEM)

Publication Link

https://iceem2021.conferences.ekb.eg/

Details

Type

Article

Locale

en

Created At

13 Dec 2022