The existence of large image datasets such as the set of photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labelled pixels. These classes exhibit a surprising degree of separability which we exploit by building a skin pixel detector achieving a detection rate of 80% with 8.5% false positives. We compare the performance of histogram and mixture models in skin detection and find histogram models to be superior in accuracy and computational cost. Using aggregate features computed from the skin pixel detector we build a surprisingly effective detector for naked people. Our results suggest that color can be a more powerful cue for detecting people in unconstrained imagery than was previously suspected. We believe this work is the most comprehensive and detailed exploration of skin color models to date.