Face2gps: Estimating Geographic Location From Facial Features

The facial appearance of a person is a product of many factors, including their gender, age, and ethnicity. Methods for estimating these latent factors directly from an image of a face have been extensively studied for decades. We extend this line of work to include estimating the location where the image was taken. We propose a deep network architecture for making such predictions and demonstrate its superiority to other approaches in an extensive set of quantitative experiments on the GeoFaces dataset. Our experiments show that in 26% of the cases the ground truth location is the topmost prediction, and if we allow ourselves to consider the top five predictions, the accuracy increases to 47%. In both cases, the deep learning based approach significantly outperforms random chance as well as another baseline method.