Optimized High- Resolution Network for Accurate Facial Wrinkle Detection Using Harbor Seal Whiskers Optimization.

Abstract

Background: Automated facial wrinkle detection is relevant to facial-ageing assessment, cosmetic analysis, dermatological screening, and personalized skincare. However, wrinkles are thin, low-contrast, and spatially irregular structures whose appearance may be affected by illumination, pose, skin texture, age, image quality, and demographic characteristics.

Objective: This study proposes FWD-DHRN-HSWOA, a facial wrinkle-detection framework combining attentional image enhancement, high-resolution feature representation, and metaheuristic parameter optimization.

Methods: Facial images were obtained from the FG-NET Aging Database and supplemented with study-specific wrinkle annotations because wrinkle labels are not native to FG-NET. Images were aligned, resized, normalized, and enhanced using Deep Attentional Guided Image Filtering. A Dynamic Lightweight High-Resolution Network was then used to preserve fine spatial information during wrinkle localization. Harbor Seal Whiskers Optimization was applied as an outer-loop procedure for tuning selected model parameters. The proposed method and implemented baselines were evaluated using the same data partitions, preprocessing conditions, and performance metrics.

Results: Within the evaluated FG-NET-derived annotation setting, the proposed framework produced higher observed accuracy, precision, recall, F1-score, and specificity and lower RMSE than the implemented comparison models. These results reflect performance under the reported experimental setting and should not be interpreted as evidence of equivalent performance across unrepresented demographic groups or dedicated clinical wrinkle datasets.

Conclusion: Combining attentional preprocessing, high-resolution feature extraction, and metaheuristic optimization shows promise for fine facial wrinkle localization. Nevertheless, external validation on dedicated wrinkle datasets with verified age, ethnicity, skin-tone, and acquisition-condition diversity is necessary before broad deployment.