Abstract 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 (\(e = 1.71\)) and wrinkles (\(e = 1.40\)). While achieving satisfactory results for under-eye circles (\(e = 0.32\)), redness (\(e = 2.22\)), and shine (\(e = 0.23\)), the baseline models encountered significant architectural limitations when resolving highly localized or imbalanced features such as acne (\(e = 2.59\)), pore size (\(e = 2.57\)), and pigment spots (\(e = 2.20\)), the latter three underperforming a trivial constant-mean predictor. Similar content being viewed by others Subjects Introduction Deep learning algorithms, particularly convolutional neural networks, have emerged as a dominant force in medical image analysis1,2,3. Their superior performance in tasks like image classification, segmentation, and object detection has significantly advanced the field1,2,3. Medical image classification (MIC), a fundamental task involving the categorization of medical images into predefined classes (e.g., health status, tumor type), is a prime application area for these models. Accurate MIC can support clinical decision-making and improve diagnostic efficiency4,5.