Combining global and local vision foundation models for explainable tattoo matching

Tattoo matching is important for criminal investigations. Recently, vision foundation models have shown increased performance for tasks like image classification and image retrieval. Global vision foundation models (e.g., CLIP) or local approaches (e.g., OmniGlue) show increas ed performance for image retrieval. In this paper,
we show the added value of combining global and local approaches for explainable tattoo matching. We also investigate the use of a sketchification approach, facilitating the matching process for more abstract tattoos.
We finally highlight the potential of local OmniGlue as a more explainable alternative to global image-based matching methods like CLIP.