MicroGlam: Microscopic Skin Image Dataset with Cosmetics
This provides a dataset for researchers in computer vision and cosmetics to improve cosmetic rendering, but it is incremental as it builds on existing image-to-image translation techniques.
The authors tackled the problem of cosmetic rendering on skin by introducing a new microscopic skin image dataset captured under diverse lighting conditions, and demonstrated its viability with an image-to-image translation pipeline that outperformed an existing method.
In this paper, we present a cosmetic-specific skin image dataset. It consists of skin images from $45$ patches ($5$ skin patches each from $9$ participants) of size $8mm^*8mm$ under three cosmetic products (i.e., foundation, blusher, and highlighter). We designed a novel capturing device inspired by Light Stage. Using the device, we captured over $600$ images of each skin patch under diverse lighting conditions in $30$ seconds. We repeated the process for the same skin patch under three cosmetic products. Finally, we demonstrate the viability of the dataset with an image-to-image translation-based pipeline for cosmetic rendering and compared our data-driven approach to an existing cosmetic rendering method.