Abstract

The images uploaded to social networking websites are a rich source of information about the appearance of people around the world. We present a system, GeoFaceExplorer, for collecting, processing, browsing, and analyzing this data. GeoFaceExplorer allows for the crowdsourced collection of human facial images, as well as automated and interactive visual analysis of the geo-dependence of facial appearance and visual attributes, such as ethnicity, gender, and whether or not a person is wearing glasses. As a case study, automated approaches are applied to detect common facial attributes in a large set of geo-tagged human faces, leading to several analysis results that illuminate the relationship between raw facial appearance, facial attributes, and geographic location. We show how the distribution of these attributes differs in ten major urban areas. Our analysis also shows a similar expected distribution of ethnicity within large urban areas in comparison to manually collected U.S. census data. In addition, by applying automated hierarchical clustering to facial attribute similarity, we find a large degree of overlap between discovered regional clusters and geographical and national boundaries.

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