Automated Facial Wrinkle Segmentation for Dermatological Assessment Using VGG-Based U-Net with Hybrid Augmentation

Wahyu Fajar Setiawan; Nanik Suciati

doi:10.52436/1.jutif.2026.7.2.5561

Authors

Wahyu Fajar Setiawan Department of Informatic, Sepuluh Nopember Institute of Technology (ITS), Indonesia
Nanik Suciati Department of Informatic, Sepuluh Nopember Institute of Technology (ITS), Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5561

Keywords:

Data Augmentation, Deep Learning, Facial Wrinkle Segmentation, Transfer Learning, U-Net Architecture

Abstract

Manual and automated facial wrinkle segmentation remains challenging due to the fine-grained nature of wrinkles, uneven distribution across facial regions, severe class imbalance (~2% wrinkle pixels), and sensitivity to lighting variations—limiting the reliability of existing dermatological assessment tools. This study aims to evaluate VGG transfer learning with hybrid augmentation strategies for U-Net-based automated facial wrinkle segmentation. Using the FFHQ-Wrinkle dataset comprising 1,000 manually annotated high-resolution images (1024×1024 pixels), this study systematically evaluates three U-Net variants (Baseline, VGG16-based, VGG19-based) across four augmentation strategies: no augmentation, hierarchical image enhancement (CLAHE, gamma correction, bilateral filtering), geometric transformation (rotation, translation, shear, zoom, flip), and hybrid combination. A multi-component loss function integrating Focal Loss, Dice Loss, IoU Loss, and Boundary Loss addresses class imbalance while optimizing both region overlap and edge localization. The proposed VGG19-based U-Net with hybrid augmentation achieves state-of-the-art performance: Dice coefficient of 0.6585, IoU of 0.4970, precision of 0.6186, recall of 0.7344, and Boundary F1 of 0.9185. Key findings demonstrate that VGG19 transfer learning provides +21.54% Dice improvement over Baseline U-Net with 12.7-fold reduction in overfitting, while hybrid augmentation yields +4.87% Dice improvement with +2.24% synergistic gain beyond individual strategies. This research advances automated dermatological tools for precise skin health assessment, reducing subjectivity in clinical evaluations and providing actionable guidelines for practitioners developing automated wrinkle analysis systems.

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