<![CDATA[The model integrates a U-Net-based generator with an occlusion-aware parsing branch and a multi-scale discriminator, optimized using a composite loss that incorporates adversarial, identity, perceptual, and mask terms. To rigorously evaluate the model, a new dataset comprising more than 4000 face images was constructed, capturing a diverse range of real-world occlusions, including natural, sunglasses, hand, and mask types. Empirical analyses on this dataset, as well as on standard benchmarks (CelebA-HQ, FFHQ, and Multi-PIE), demonstrate that CFR-GAN achieves consistently high scores across key metrics such as Verification Accuracy (ACC), Area Under Curve (AUC), True Acceptance Rate at low False Acceptance Rates (TAR@FAR), and Normalized Mean Error (NME), often surpassing competitive methods. While the model shows strong generalization for varied occlusions, extreme cases and rare occlusion patterns may still pose challenges, particularly given the reliance on the quality of the 3DMM regressor and inherent dataset diversity. The self-supervised design eliminates the need for paired, labeled training data, allowing for enhanced adaptability; however, more work remains to assure robust performance under highly complex or previously unseen occlusions and to validate cross-demographic generalization. Overall, by combining innovative self-supervised signals with a broad, custom dataset and comprehensive quantitative analysis, CFR-GAN presents a practical step forward for real-world occlusion-robust face recognition. Future improvements could include statistical analysis of failure modes, in-depth ablation of loss components, and public release of code and datasets to support reproducibility and further benchmarking.]]>
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