<![CDATA[In recent years, face recognition systems have gained significant traction due to their applications in security, surveillance, and user authentication. Despite the advances in deep learning techniques, challenges such as varying lighting conditions, occlusions, and facial expressions continue to affect the robustness and efficiency of these systems. This paper proposes a novel approach to face recognition based on Adaptive Resonance Theory (ART). ART's ability to adaptively learn and recognize patterns in a stable and incremental manner makes it particularly suitable for handling the dynamic variations encountered in face recognition tasks. Our proposed ART-based face recognition framework is evaluated on multiple benchmark datasets, demonstrating superior performance in terms of accuracy, robustness to noise, and computational efficiency compared to traditional methods. The experimental results highlight the potential of ART to enhance the reliability of face recognition systems in real-world applications.]]>
