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Similar to the majority of deep learning applications, diagnosing skindiseases using computer vision and deep learning often requires a large volumeof data. However, obtaining sufficient data for particular types of facial skinconditions can be difficult due to privacy concerns. As a result, conditionslike Rosacea are often understudied in computer-aided diagnosis. The limitedavailability of data for facial skin conditions has led to the investigation ofalternative methods for computer-aided diagnosis. In recent years, GenerativeAdversarial Networks (GANs), mainly variants of StyleGANs, have demonstratedpromising results in generating synthetic facial images. In this study, for thefirst time, a small dataset of Rosacea with 300 full-face images is utilized tofurther investigate the possibility of generating synthetic data. Thepreliminary experiments show how fine-tuning the model and varying experimentalsettings significantly affect the fidelity of the Rosacea features. It isdemonstrated that $R_1$ Regularization strength helps achieve high-fidelitydetails. Additionally, this study presents qualitative evaluations ofsynthetic/generated faces by expert dermatologists and non-specialistparticipants. The quantitative evaluation is presented using a few validationmetric(s). Furthermore a number of limitations and future directions arediscussed. Code and generated dataset are available at:\url{https://github.com/thinkercache/stylegan2-ada-pytorch}

High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data