The question everybody asks
What is the rarest face shape? Nobody actually knows
There is no peer-reviewed prevalence distribution for oval, round, square, heart, diamond, oblong and triangle in any human population. Not a small one, not an old one. Every percentage you have read comes from a face-shape tool's own user logs, and they contradict each other so violently that they cannot all be describing the same species.
Face shape prevalence figures published by four tool sites, with the source each one cites| Site | Faces claimed | Oval | Diamond | Source cited |
|---|
| thefacereport.com | 15,429 | 49.2% | 5.4% | none |
|---|
| faceauraai.com (newer) | 18,041 | 38.8% | 25.0% | none |
|---|
| faceauraai.com (older) | 3,803 | ~46% | 22% | none |
|---|
| oblongfaceshape.com | 51,247+ | 28.4% | ~5% | Farkas 1994 |
|---|
Diamond ranges from about 5% to 25%, a five-fold disagreement, and one site calls it the rarest shape while another's own data puts it second most common. Triangle is reported at 0.03%, 0.05% and 8.6% by three different tools. The one citation anybody offers, Farkas 1994, is an anthropometry reference that publishes linear and angular norms; it does not contain frequencies of "oval" or "heart" at all.
Worth saying who agrees: one of those sites states plainly that face-shape categories are "a styling convention, not a rigorous scientific construct." That is correct, and it sits on the same site as a 49.2% figure.
Where the seven categories came from
Not from anatomy. The machine-vision literature on face shape uses five classes: heart, oblong, oval, round, square. Triangle and diamond are additions from styling sites, which is why their reported prevalence swings by two orders of magnitude. The taxonomy itself traces to eyewear retail: a published survey of the field notes that OPSM Opticians recognised four "traditional and archaic" shapes, then commissioned work in 2014 proposing five more (kite, rectangle, teardrop, heptagon and oblong), a nine-class scheme that it records as "yet to become mainstream." The number of face shapes is unsettled at four, five, six, seven or nine depending on who is selling what.
The taxonomy that does have published numbers
Physical anthropology has measured face proportions for a century, using the morphological facial index: face height divided by cheekbone width, times 100, with five named bands. It is replicable, caliper-measurable and has real population distributions. Here is what it finds.
Nepal, N=173
dental students 17 to 25, sliding caliper
Leptoprosopic
40.5%
most common band
India, N=200
medical students, mean age 20.1
Mesoprosopic
34.5%
most common band
Latvia, N=375
healthy children aged 1 to 15
Hypereuryprosopic
45.5%
most common band
Three samples, three different answers. The most common face is long and narrow in the Nepali students, middling in the Indian students, and short and wide in the Latvian children, where the index also climbs steadily with age, from 70.1 at age one to 82.3 by fifteen. There is no single most common face. There is a distribution that moves with population and with age, which is a real answer, and it is the opposite of a league table.
How often do two sources agree on the same face?
About half the time. A University of the Philippines study building a face-shape classifier checked celebrities whose shape is published in multiple places, and found that 62 of 117 names had conflicting classifications across the 21 sites reviewed. Same faces, same seven labels, 53% disagreement. That is the measured version of the thing competitors gesture at when they say "most people fall between two shapes" without ever putting a number on it.
The same paper is candid about where an "in-between" threshold comes from: a face is called a blend when a blending score lands between 40% and 60%. That is a number an engineer chose. And the best published classifier in that work agrees with its own human labellers only about 85% of the time, on five classes rather than seven. Any page handing you a seven-way categorical verdict as fact is claiming a precision no published method has demonstrated.
This is why the result above shows you every shape's similarity rather than one label, and why a borderline face is reported as borderline. The uncertainty is real, it is documented, and hiding it behind a single word would be the easiest thing on this page to get wrong.
Sources: Shrestha et al., JNMA 2019 (Nepal, N=173) · Choudhary et al., Cureus 2026 (India, N=200) · Grabcika et al., Children 2024 (Latvia, N=375) · Tio, "Face shape classification using Inception v3," University of the Philippines Diliman (N=500). Tool-site figures as published by each site on the date reviewed.
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