Smart AI-Based Facial Skin Assessment Using SR-GAN and Mask R-CNN

Facial skin assessment plays a critical role in early dermatological diagnosis, cosmetic analysis, and personalized skincare recommendations. Traditional skin assessment methods rely heavily on dermatologists’ expertise and high-resolution imaging, which may not always be available. Low-resolution images, noise, uneven illumination, and inaccurate segmentation reduce the quality of automated analysis. To address these limitations, this research proposes a Smart AI-based facial skin assessment model using Super-Resolution Generative Adversarial Networks (SR-GAN) to enhance image quality and Mask R-CNN to segment the facial skin region accurately. SR-GAN reconstructs high-resolution skin textures from low-resolution inputs, enabling precise identification of pores, acne, wrinkles, and pigmentation. Mask R-CNN extracts skin regions by detecting and segmenting relevant facial areas while removing background, hair, and noise. The system integrates insights from deep learning research on skin lesion segmentation, CNN-RNN fusion models, and SRGAN-based facial enhancement. The proposed methodology significantly improves clarity, segmentation accuracy, and diagnostic capability, making it suitable for tele-dermatology, skincare applications, and mobile-based skin assessment tools.