Enhanced Reconstruction of Occluded Images Using GAN and VGG-Net Preprocessing

Facial recognition is widely used in security and identification systems, but occlusions like masks or glasses remain a major challenge. Recent approaches, such as GANs and partial feature extraction methods, attempt to reconstruct or identify occluded facial images. However, these approaches still have limitations in handling severe occlusions, computational efficiency, and dependency on large labeled datasets. In this paper, a GAN-based framework for synthetic reconstruction of occluded facial images is proposed, incorporating multiple specialized modules including a VGG-Net-based perceptual loss component to enhance visual quality. Our architecture improves the fidelity and robustness of reconstructed faces under varied occlusion types. Experimental evaluation on different occlusion scenarios demonstrated high reconstruction quality, with PSNR up to 33.106 and SSIM up to 0.983. The model also maintained strong recognition performance across diverse occlusion combinations. These findings support the framework's potential to enhance face recognition systems in real-world, unconstrained environments.