Do not smile when acquiring consumer selfies for multi-attribute skin profiling and baseline deep learning evaluation

The increasing demand for personalized skincare solutions highlights a significant gap: many consumers struggle to find suitable products without professional guidance. While the commercial potential for tailored product recommendations is vast, a key challenge remains the lack of effective methods for skin profiling via image classification. To address this challenge, this paper introduces a comprehensive benchmark dataset of 3203 standardized facial consumer selfies, annotated across eight primary cosmetic skin features. We establish a transparent baseline evaluation utilizing the open-source medical image classification framework AUCMEDI. A variety of deep learning architectures were trained to recognize and classify various skin features, including sagging skin, wrinkles, under-eye circles, redness, shine, pigment spots, acne, and pore size. The baseline model's performance was evaluated using the mean absolute error (e), which appropriately accounts for the ordinal distance in cosmetic grading. Utilizing standard deep learning architectures, the baseline benchmark established promising performance in structural categories like sagging skin ([Formula: see text]) and wrinkles ([Formula: see text]). While achieving satisfactory results for under-eye circles ([Formula: see text]), redness ([Formula: see text]), and shine ([Formula: see text]), the baseline models encountered significant architectural limitations when resolving highly localized or imbalanced features such as acne ([Formula: see text]), pore size ([Formula: see text]), and pigment spots ([Formula: see text]), the latter three underperforming a trivial constant-mean predictor.