Human faces have been a subject of study in computer science for decades. The rich of set features from human faces have been used in solving various problems in computer vision, including person identification, facial expression analysis, and attribute classification. In this work, I explore the human facial features that depend on the geo-location using a data- driven approach. I analyze millions of public domain images to extract the geo-dependent human facial features and explore their applications. Using various machine learning and statistical techniques, I show that the geo-dependent features of human faces can be used to solve the image geo-localization task of given an image, predict where it was taken. Deep Convolutional Neural Networks (CNN) have been recently shown to excel at the image classification task; I have used CNNs to geo-localize images using the human face as a cue. I also show that the facial features used in image localization can be used to solve other problems, such as ethnicity, gender, and age estimation.