In intelligent human environments, image sensors are frequently hindered by occlusions, such as masks, which degrade the reliability of facial data for security and interactive support systems. In this paper, we propose a unified framework designed as a robust support system that integrates an occlusion segmentation network with a face image restoration network. To facilitate deployment in resource-constrained sensing nodes, the segmentation network employs depthwise separable convolutions to ensure computational efficiency while leveraging residual connections for multi-scale feature fusion. On the basis of precisely localized occluded areas, a generative adversarial network is introduced to reconstruct facial structures with high fidelity. The generator incorporates two novel feature enhancement components: a hybrid attention aggregation module, which strengthens global semantic consistency within skip connections, and a multi-scale spatial attention module, designed to capture fine-grained textures from sensor data across different spatial scales. Experimental results on the CelebFaces Attributes High-Quality (CelebA-HQ) dataset demonstrate that the proposed system effectively restores masked facial regions, achieving a PSNR of 35.01 dB and an SSIM of 0.931 under challenging 35–45% occlusion ratios. By significantly enhancing visual fidelity and recognition robustness, this framework provides a reliable solution for real-world vision-based support systems in human-centric environments.