How a face with South Asian ancestry scores across 17 structural metrics. Descriptive anthropometry, not a hierarchy.
Beauty is multi-ethnic. The published cross-cultural preference research is clear: no population scores higher than another in aggregate. This page describes the distribution, not a verdict.
17 metrics · Multi-ethnic norms · Free · No signup
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Kumari et al.'s work on North Indian samples and the parallel South Indian datasets document a measurable craniofacial split inside the South Asian category. North Indian, Punjabi, and Pakistani Indo-Aryan populations cluster around one set of nasal-index and midface-projection means; Tamil, Telugu, Kannada, and Sri Lankan Dravidian populations cluster around another. The within-region difference is large enough that a Mumbai user and a Chennai user scored against the same regional aggregate will see different gaps between their universal percentile and their sub-population percentile.
The lip and nasal metrics carry the biggest implications. South Asian-distribution lip-vermilion thickness norms place fuller upper and lower lips at the population mean rather than as a European-distribution deviation. South Asian-distribution nasal-index norms run wider than European baselines but narrower than the African-distribution mean Coetzee 2014 documented. Same geometry, different reference axis, different read.
Palpebral fissure angle is the metric most distinct to the South Asian category. The Ngeow and Aljunid 2009 Malaysian-population data plus Kumari's Indian samples both record a slightly steeper average canthal tilt than European norms, which on a generic European-default tool reads as a percentile bump that is actually just the population mean. The dual-percentile output is the only way to separate "your face is above the South Asian distribution mean on canthal tilt" from "the European norm happens to be lower than the South Asian norm on this metric."
Published South Asian nasal-width-to-height norms (Kumari et al. on Indian populations) sit in a distinct sub-range from European norms. The population-appropriate percentile prevents the European mean from being treated as a universal target.
Average upper and lower lip thickness sits higher than European norms. The published distributions place this as population mean rather than as a deviation, so the lip metric reads at average percentile against the appropriate reference.
Average canthal tilt sits in a distinct range. The eye aperture geometry registers slightly differently and the population-appropriate norm carries this as descriptive context.
Lower-face height relative to total facial height clusters in a distinct sub-range. Carried as descriptive percentile rather than as a deviation from European norms.
Denser brow hair and a typically lower hairline shift the brow-to-eye distance metric. The score treats this as structural signal rather than a styling artifact.
Skin tone is not a structural metric in the composite. What it affects is photo-quality requirements; the detector needs even, diffuse light to place landmarks accurately on darker skin, and a poorly lit photo will give a lower-confidence read regardless of underlying geometry.
Hiding the universal percentile would protect the user from an unflattering number but at the cost of the most honest read on a question some users genuinely want answered: where do I sit relative to faces in general. We show both. The universal percentile reflects the cross-population dataset; the population-appropriate percentile reflects the published distribution for similar ancestry. Use whichever is more useful for the question you brought to the page.
South Asia is itself a continuum. The South Asian-distribution norms aggregate North Indian, South Indian, Punjabi, Bengali, Sri Lankan, and many other populations with their own distinct craniofacial distributions. The population-appropriate percentile should be read as directional rather than as a precise read.
Free score is the headline. Population-appropriate context is the plan.
The $14.99 Looksmax Report scores all 17 metrics with both universal and South-Asian-distribution percentiles where validated norms exist, identifies your two weakest, and writes a soft-tissue-first plan.
Free, instant, private. 17 metrics with population-appropriate percentile context in the paid report.
17 metrics · Multi-ethnic norms · Photos auto-deleted
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