Thanks for finding this, the lack of explanation in the Wikipedia article was driving me crazy. Here’s what the `TeachingDemos::faces2` documentation says about

Thanks for finding this, the lack of explanation in the Wikipedia article was driving me crazy. Here’s what the `TeachingDemos::faces2` documentation says about the feature mapping: > The features are: 1 Width of center 2 Top vs. Bottom width (height of split) 3 Height of Face 4 Width of top half of face 5 Width of bottom half of face 6 Length of Nose 7 Height of Mouth 8 Curvature of Mouth (abs < 9) 9 Width of Mouth 10 Height of Eyes 11 Distance between Eyes (.5-.9) 12 Angle of Eyes/Eyebrows 13 Circle/Ellipse of Eyes 14 Size of Eyes 15 Position Left/Right of Eyeballs/Eyebrows 16 Height of Eyebrows 17 Angle of Eyebrows 18 Width of Eyebrows So there are 18 visually distinguishable features, and the code maps the columns 2–9 redundantly to two features each, and columns 10–11 to one feature each. I don’t understand how the scaling was chosen — the function requires the values to be in the interval [0, 1] but since the original scores appear to be in [0, 10], a more natural transformation would be to just divide by 10 instead of `x / 5 - 1`. Obviously these two transformations result in markedly different faces. If I find time later I’ll upload an updated plot that includes a legend. Unfortunately that’s not easy since the `faces2` function overrides R’s plot layout so there’s no space to fit the legend.