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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. The culture of the society of which the photographer is a part provides a strong context for recognizing the content of the captured images. We demonstrate one aspect of this cultural context by recognizing people from first names. The distribution of first names chosen for newborn babies evolves with time and is gender-specific. As a result, a first name provides a strong prior for describing the individual. Specifically, we use the U.S. Social Security Administration baby name database to learn priors for gender and age for 6693 first names. Most face recognition methods do not even consider the name of the individual of interest, or the name is treated merely as an identifier that provides no information about appearance. In contrast, we combine image-based gender and age classifiers with the cultural context information provided by first names to recognize people with no labeled examples. Our model uses image-based age and gender estimates for assigning first names to people and in turn, the age and gender estimates are improved.