Appling XGBoost for Advanced Face Recognition System

Authors

  • Ahmed Ihsan Ali Computer Engineering. Al-Hikma University College. Baghdad, Iraq.
  • Sura Fadhil Rahman Computer Techniques Engineering , Imam AL-Kadhun College , Al-Qadisiyah , Iraq .
  • Mustafa Abbas Salih Information technology, Islamic university, Al-Qadisiyah , Iraq.
  • Hawraa Fadhil Rahman Computer Techniques Engineering , Imam AL-Kadhun College , Al-Qadisiyah , Iraq.
  • Shaymaa Jawad Kadhun Mechanical Engineering , University of Al-Qadisiyah , Al-Qadisiyah , Iraq.

DOI:

https://doi.org/10.29304/jqcsm.2025.17.22197

Keywords:

Face Recognition, Security, XGBoost, Machine Learning, DeepFace

Abstract

Face recognition technology has significantly advanced and become essential in various applications, including security systems, human-computer interaction, and biometric authentication. This work introduces an effective face Recognition for recognizing a person from facial images. This work utilizes state-of-the-art preprocessing and augmentation techniques, histogram equalization, Gaussian blur, and Canny edge detection in enhancing quality and diversity within the data. These will ensure that all facial features are captured, including those extracted by Deep Face, HOG, LBP, and Gabor filters. Some of the machine learning methods applied and tested on this CAS-PEAL dataset include XGBoost, Decision Tree, and Random Forest. Of these, the best performance was by XGBoost, at near perfection. The outcomes hereby show that an efficient fusion of multi-feature extraction approaches with the recent machine learning methods improves the reliability of face recognition against every change in environmental conditions.

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Published

2025-06-30

How to Cite

Ihsan Ali , A., Fadhil Rahman, S., Abbas Salih, M., Fadhil Rahman, H., & Jawad Kadhun, S. (2025). Appling XGBoost for Advanced Face Recognition System. Journal of Al-Qadisiyah for Computer Science and Mathematics, 17(2), Comp. 202–214. https://doi.org/10.29304/jqcsm.2025.17.22197

Issue

Vol. 17 No. 2 (2025): JQCM

Section

Computer Articles

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Copyright (c) 2025 Ahmed Ihsan Ali , Sura Fadhil Rahman, Mustafa Abbas Salih, Hawraa Fadhil Rahman, Shaymaa Jawad Kadhun

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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