Securing Restricted Zones with a Novel Face Recognition Approach Using Face Feature Descriptors and Evidence Theory

Securing restricted zones such as airports, research facilities, and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets. Face recognition has emerged as a key biometric approach for this purpose; however, existing systems are often sensitive to variations in illumination, occlusion, and pose, which degrade their performance in real-world conditions. To address these challenges, this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant (FG-2DFLD), Generalized 2D Linear Discriminant Analysis (G2DLDA), and Modular-Local Binary Patterns (Modular-LBP) with Dempster–Shafer (DS) evidence theory for decision fusion. The proposed framework extracts global, structural, and local texture features, models them using Gaussian distributions to estimate belief factors, and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors. Experimental validation was performed on two widely used benchmark datasets, ORL and Cropped Yale B, achieving recognition rates exceeding 98%, which outperform traditional methods as well as recent deep learning-based approaches. Furthermore, the method demonstrated strong robustness under noisy conditions, maintaining accuracies above 96% with salt-and-pepper and Gaussian noise. These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy, reliability, and resilience compared to single-descriptor and conventional fusion methods. Given its high performance and efficiency, the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems, secure facility access, and other high-security domains.