Caucasoid Metric Types
by Dienekes Pontikos

Five metric types were revealed by clustering 152 examples from Coon (1939) based on 9 variables of the head. Clustering was performed on the standardized data matrix using the K-Means algorithm and employing the Euclidean measure of distance (Duda et al., 2001). In the following table, the features of the average individual from each of the five types are enumerated.

Note that "extremely" and "very" refer respectively to more than one standard deviation and 0.6 standard deviations from the Caucasoid mean. "Average" refers to within 0.3 standard deviations of the Caucasoid mean. The absence of a qualification indicates a value between 0.3 and 0.6 standard deviations from the Caucasoid mean. Thus, for example the value of the head length from shorter to longer goes from "extremely short", "very short", "short", "average", "long", "very long", "extremely" long.

Head Lengthvery longshortaveragelongvery short
Head Widthvery widevery narrowwidevery narrowwide
Minimum Frontalextremely widenarrowwidenarrowaverage
Bizygomaticextremely widevery narrowvery widevery narrowaverage
Bigonialextremely widevery narrowwidenarrowaverage
Total Facial Heightvery highvery lowlowhighvery high
Upper Facial Heightvery highvery lowvery lowhighvery high
Nasal Heighthighvery lowlowaveragevery high
Nasal Breadthextremely wideaverageaveragenarrowaverage
Cephalic Index
100*Head Breadth/Head Length
averageaveragehighvery lowvery high
Facial Index
100*Total Facial Height/Bizygomatic
averageaveragevery lowvery highhigh
Upper Facial Index
100*Upper Facial Height/Bizygomatic
averageaveragevery lowvery highhigh
Nasal Index
100*Nasal Breadth/Nasal Length
highhighaveragelowvery low
Zygo-Frontal Index
100*Minimum Frontal/Bizygomatic
Zygo-Gonial Index
very highlowaverageaverageaverage
Fronto-Parietal Index
100*Minimum Frontal/Head Breadth

* * *

Short descriptions of the five metric types are followed by a presentation of three examples closest to the mean of the corresponding cluster. The Mahalanobis generalized distance (D2) to the cluster mean is given in parentheses.


The Proto-Europoid type is distinguished by its greater size. In proportions Proto-Europoids are average with a few distinguishing traits: a wide forehead, a very wide jaw and a wide, but not short nose.

Greek (1.26)

Hungarian (3.16)

Armenian (3.54)


In contrast to the Proto-Europoid, the Mediterranoid type is distinguished by its smaller size. Mediterranoids are also average in proportions, but have less narrow noses, compressed (narrow) jaws, and a forehead that appears wide relative to their narrow face.

English (0.71)

Spanish (2.16)

Moroccan (2.68)


Alpinoids are average in size but differ markedly from the average Caucasoid in proportions. They are broader-headed and much broader- and shorter-faced in both the upper and lower portions of their face. Their foreheads appear to be narrow, both with respect to their wide heads and to their very wide faces, even though they are absolutely wide in size. The Alpinoids have wide jaws.

Ukrainian (0.73)

Hungarian (0.79)

Austrian (1.18)


Irano-Nordoids are average in size and strikingly different from the Alpinoids in proportions: the two can be said to be opposites of each other. Irano-Nordoids are very long-headed and their faces appear to be very long in both the upper and lower parts. They also have seemingly narrow noses, which is due to the small breadth, rather than the great length of the nose.

Irish (1.26)

Yemeni (1.29)

Italian (1.48)


Dinaroids are average in size and very short-headed. Like the Irano-Nordoids they are narrow-faced, but to a smaller degree. Unlike the Irano-Nordoids with their very narrow face, Dinaroid faces appear narrow due to their excessive length. Dinaroids have noses which appear to be narrow; in their case it is the great length, and not the small breadth of the nose which creates this impression.

Armenian (0.81)

Slovak (0.83)

Finn (1.44)

Simple Measurement Guide


Coon, C. S. (1939) The Races of Europe, MacMillan, New York.
Duda, R. O., Hart, P. E., Stork, D. G. (2001) Pattern Classification, 2nd ed., John Wiley & Sons, New York.