Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4031 2019 2019 Department of Computer, College of Education for Pure Sciences, Wasit University, Al-Kut, 52001, Wasit, Iraq Riyadh Riyadh College of Computer Science and Information Technology, Wasit University, Al-Kut, 52001, Wasit, Iraq Ilyas Khudhair Yalwi College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq Ali Hakem Alsaeedi College of Computer Science and Information Technology, Wasit University, Al-Kut, 52001, Wasit, Iraq; Cybersecurity Research Centre, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia Riyadh Rahef Nuiaa Alogaili Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq Mazin Abed Mohammed Face recognition in unconstrained environments is difficult due to varying poses and lighting conditions. This can severely impair the performance of intelligent recognition models. Traditional methods often do not adapt well to these variations, which results in poor performance and limited applicability. This paper proposes a hybrid intelligent face recognition model based on hierarchical feature extraction and illumination normalization (H-FR). The proposed method employs a hierarchical feature extraction model to capture macro and micro facial details, ensuring reliable recognition across diverse poses and lighting conditions. Employing Adaptive Histogram Equalization on the A and B channels of the LAB colour space effectively normalizes illumination variations, enhancing the visibility and consistency of facial features. The proposed model has been tested and validated on the "Pins Face Recognition" dataset available on Kaggle, which encompasses various celebrity faces captured in varying poses and lighting conditions. The proposed model has been demonstrated through extensive experimentation to outperform AlexNet and VGG-19. The compared algorithms achieved accuracies of 88% for AlexNet and 93% for VGG-19, while the proposed H-FR model achieved 96%. 2026 2026 01 13 10.54216/JCIM.170201 https://www.americaspg.com/articleinfo/2/show/4031