Geotagged imagery, from satellite, aerial, and ground-level cameras, provides a rich record of how the appearance of scenes and objects differ across the globe. Modern web- based mapping software makes it easy to see how different places around the world look, both from satellite and ground-level views. Unfortunately, interfaces for exploring how the appearance of objects depend on geographic location are quite limited. In this work, we focus on a particularly common object, the human face, and propose learning generative models that relate facial appearance and geographic location. We train these models using a novel dataset of geotagged face imagery we constructed for this task. We present qualitative and quantitative results that demonstrate that these models capture meaningful trends in appearance. We also describe a framework for constructing a web-based visualization that captures the geospatial distribution of human facial appearance.
['Zachary Bessinger', 'Chris Stauffer', 'Nathan Jacobs']
Who goes there?: approaches to mapping facial appearance diversity