Machine Learning Approach for Facial Image Detection System - Al-Dabbas, Hind Moutaz; Azeez, Raghad Abdulaali; Ali, Akbas Ezaldeen (Iraqi Journal of Science, 2023)
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.