Abstract
Recognizing people in images is one of the foremost challenges in computer vision. It is important to remember that consumer photography has a highly social aspect. The photographer captures images not in a random fashion, but rather to remember or document meaningful events in her life. Understanding images of people necessitates that the context of each person in an image is considered. Context includes information related to the image of the scene surrounding the person, camera context such as location and image capture time, and the social context that describes the interactions between people. The goal of this paper is to provide the computer with the same intuition that humans would use for analyzing images of people. Fortunately, rather than relying on a lifetime of experience, context can often be modeled with large amounts of publicly available data. Probabilistic graph models and machine learning are used to model the relationship between people and context in a principled manner.
