Machine Learning Approach for Facial Image Detection System

Hind Moutaz Al-Dabbas; Raghad Abdulaali Azeez; Akbas Ezaldeen Ali

doi:10.24996/ijs.2023.64.10.44

Authors

Hind Moutaz Al-Dabbas Department of Computer Science, College of Education for Pure Science/Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0002-3912-2051
Raghad Abdulaali Azeez Information Technology Unit, College of Education for Human Science-Ibn-Rushed, University of Baghdad, Baghdad, Iraq
Akbas Ezaldeen Ali Department of Computer Science, University of Technology, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2023.64.10.44

Keywords:

face detection, LDA, J48, OneR, JRip

Abstract

Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy.