<![CDATA[Facial wrinkle segmentation is critical for automated dermatological assessment, yet existing deep learning methods exhibit significant performance degradation under real-world illumination and pose variations, restricting practical clinical deployment where imaging conditions cannot be controlled. This study proposes a novel robustness-oriented segmentation framework that integrates three synergistic components: (1) attention-enhanced U-Net architectures with strategically frozen VGG16/VGG19 backbones enabling hierarchical feature transfer, (2) a dual augmentation strategy coupling geometric transformations for pose invariance with a four-level photometric enhancement pipeline for illumination robustness, and (3) a weighted mask fusion mechanism combining expert annotations with weak supervision labels. Three architectures (baseline Attention U-Net, VGG16, and VGG19 variants) are trained on 1,000 FFHQ-Wrinkle images and systematically evaluated across four augmentation strategies under nine challenging deployment conditions, including low light, high contrast, noise, head tilts, and perspective shifts. The proposed VGG19 Attention U-Net with combined augmentation achieves a Dice coefficient of 0.6533 and IoU of 0.4931, outperforming the best existing method (Striped WriNet) by +4.26% in Dice and +5.89% in IoU under identical re-implemented training conditions. The model retains 97.82% of its original performance across all nine perturbation conditions (robustness score: 0.6391), representing a 10.4% robustness improvement over the non-augmented baseline. These results demonstrate that the synergistic combination of attention mechanisms, transfer learning, and dual augmentation produces clinically viable robustness for facial wrinkle segmentation.]]>
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