A Hybrid Intelligent Facial Recognition Model Based on Hierarchical Feature Extraction and Il-lamination Normalization
Ali F. Rashid1, Ilyas Khudhair Yalwi2, Ali Hakem Alsaeedi3, Riyadh Rahef Nuiaa Alogaili2,4,*,
Mazin Abed Mohammed5
1Department of Computer, College of Education for Pure Sciences, Wasit University, Al-Kut, 52001, Wasit, Iraq
2College of Computer Science and Information Technology, Wasit University, Al-Kut, 52001, Wasit, Iraq
3College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq
4Cybersecurity Research Centre, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
5Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq
Emails: alirashid@uowasit.edu.iq; ilyas@uowasit.edu.iq; ali.alsaeedi@qu.edu.iq; riyadh@uowasit.edu.iq; mazinalshujeary@uoanbar.edu.iq
Abstract
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%.
Keywords: Face Recognition; Hybrid Feature extraction; Machine learning; Lumination Normalization; Edge enhancement